Alternatives to NVIDIA PhysicsNeMo
Compare NVIDIA PhysicsNeMo alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to NVIDIA PhysicsNeMo in 2026. Compare features, ratings, user reviews, pricing, and more from NVIDIA PhysicsNeMo competitors and alternatives in order to make an informed decision for your business.
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NVIDIA Modulus
NVIDIA
NVIDIA Modulus is a neural network framework that blends the power of physics in the form of governing partial differential equations (PDEs) with data to build high-fidelity, parameterized surrogate models with near-real-time latency. Whether you’re looking to get started with AI-driven physics problems or designing digital twin models for complex non-linear, multi-physics systems, NVIDIA Modulus can support your work. Offers building blocks for developing physics machine learning surrogate models that combine both physics and data. The framework is generalizable to different domains and use cases—from engineering simulations to life sciences and from forward simulations to inverse/data assimilation problems. Provides parameterized system representation that solves for multiple scenarios in near real time, letting you train once offline to infer in real time repeatedly. -
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NVIDIA Parabricks
NVIDIA
NVIDIA® Parabricks® is the only GPU-accelerated suite of genomic analysis applications that delivers fast and accurate analysis of genomes and exomes for sequencing centers, clinical teams, genomics researchers, and high-throughput sequencing instrument developers. NVIDIA Parabricks provides GPU-accelerated versions of tools used every day by computational biologists and bioinformaticians—enabling significantly faster runtimes, workflow scalability, and lower compute costs. From FastQ to Variant Call Format (VCF), NVIDIA Parabricks accelerates runtimes across a series of hardware configurations with NVIDIA A100 Tensor Core GPUs. Genomic researchers can experience acceleration across every step of their analysis workflows, from alignment to sorting to variant calling. When more GPUs are used, a near-linear scaling in compute time is observed compared to CPU-only systems, allowing up to 107X acceleration. -
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FEATool Multiphysics
Precise Simulation
FEATool Multiphysics - "Physics Simulation Made Easy" - a fully integrated physics, FEA, and CFD simulation toolbox. FEATool Multiphysics is a fully integrated simulation platform with a unified interface for several Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD) multi-physics solvers, such as OpenFOAM, SU2 Code, and FEniCS. This uniquely allows for modeling coupled physics phenomena such as found in fluid flow, heat transfer, structural, electromagnetics, acoustics, and chemical engineering applications, within a single user-friendly interface. With these capabilities, FEATool Multiphysics has become trusted tool by engineers and researchers worldwide to accelerate innovation and quickly achieve results in the energy, automotive, semi-conductor, and process industries. -
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COMSOL Multiphysics
Comsol Group
Simulate real-world designs, devices, and processes with multiphysics software from COMSOL. General-purpose simulation software based on advanced numerical methods. Fully coupled multiphysics and single-physics modeling capabilities. Complete modeling workflow, from geometry to postprocessing. User-friendly tools for building and deploying simulation apps. The COMSOL Multiphysics® software brings a user interface and experience that is always the same, regardless of engineering application and physics phenomena. Add-on modules provide specialized functionality for electromagnetics, structural mechanics, acoustics, fluid flow, heat transfer, and chemical engineering. Choose from a list of LiveLink™ products to interface directly with CAD and other third-party software. Deploy simulation applications with COMSOL Compiler™ and COMSOL Server™. Create physics-based models and simulation applications with this software platform. -
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NVIDIA Isaac Sim
NVIDIA
NVIDIA Isaac Sim is an open source reference robotics simulation application built on NVIDIA Omniverse, enabling developers to design, simulate, test, and train AI-driven robots in physically realistic virtual environments. It is built atop Universal Scene Description (OpenUSD), offering full extensibility so developers can create custom simulators or seamlessly integrate Isaac Sim's capabilities into existing validation pipelines. The platform supports three essential workflows; large-scale synthetic data generation for training foundation models with photorealistic rendering and automatic ground truth labeling; software-in-the-loop testing, which connects actual robot software with simulated hardware to validate control and perception systems; and robot learning through NVIDIA’s Isaac Lab, which accelerates training of behaviors in simulation before real-world deployment. Isaac Sim delivers GPU-accelerated physics (via NVIDIA PhysX) and RTX-enabled sensor simulation.Starting Price: Free -
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NVIDIA Clara
NVIDIA
Clara’s domain-specific tools, AI pre-trained models, and accelerated applications are enabling AI breakthroughs in numerous fields, including medical devices, imaging, drug discovery, and genomics. Explore the end-to-end pipeline of medical device development and deployment with the Holoscan platform. Build containerized AI apps with the Holoscan SDK and MONAI, and streamline deployment in next-generation AI devices with the NVIDIA IGX developer kits. The NVIDIA Holoscan SDK includes healthcare-specific acceleration libraries, pre-trained AI models, and reference applications for computational medical devices. -
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NVIDIA TensorRT
NVIDIA
NVIDIA TensorRT is an ecosystem of APIs for high-performance deep learning inference, encompassing an inference runtime and model optimizations that deliver low latency and high throughput for production applications. Built on the CUDA parallel programming model, TensorRT optimizes neural network models trained on all major frameworks, calibrating them for lower precision with high accuracy, and deploying them across hyperscale data centers, workstations, laptops, and edge devices. It employs techniques such as quantization, layer and tensor fusion, and kernel tuning on all types of NVIDIA GPUs, from edge devices to PCs to data centers. The ecosystem includes TensorRT-LLM, an open source library that accelerates and optimizes inference performance of recent large language models on the NVIDIA AI platform, enabling developers to experiment with new LLMs for high performance and quick customization through a simplified Python API.Starting Price: Free -
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XGtd
Remcom
XGtd is a ray-based electromagnetic analysis tool for assessing the effects of a vehicle or vessel on antenna radiation, predicting coupling between antennas, and predicting radar cross-section. It is ideally suited for applications with higher frequencies or very large platforms where the requirements of a full physics method may exceed available computational resources. XGtd’s capabilities extend well beyond standard ray tracing codes, incorporating techniques including Geometric Optics (GO), the Uniform Theory of Diffraction (UTD), Physical Optics (PO), and the Method of Equivalent Currents (MEC). XGtd provides high-fidelity outputs tailored to its intended applications. High-fidelity field predictions in shadow zones including creeping wave effects. Multipath calculations including reflections, transmissions, wedge diffractions, surface diffractions, and creeping waves. -
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Geminus
Geminus
Geminus unleashes the power of predictive intelligence by intersecting AI and physics with multi-fidelity modeling. Our novel, first-principles AI translates the constraints of the physical world inside resilient predictive models. The Geminus platform leverages sparse data to quickly analyze the behavior of complex industrial systems, and precisely predict the impact of decisions that drive your business forward. The Geminus multi-fidelity approach fuses models with data, which enables you to create highly accurate surrogates over 1,000x faster than simulation. Only Geminus accurately quantifies model uncertainty, so you can be confident in your predictions and the decisions they inspire. Geminus compresses model creation time from months to hours requiring far fewer data and computes resources than traditional AI, or simulation methods. Models built on Geminus are infused with an understanding of the known behavior of real-world systems. -
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Lucky Robots
Lucky Robots
Lucky Robots is a robotics-focused simulation platform that lets teams train, test, and refine AI models for robots entirely in high-fidelity virtual environments that mimic real-world physics, sensors, and interactions, enabling massive generation of synthetic training data and rapid iteration without physical robots or costly lab setups. It uses hyper-realistic scenes (e.g., kitchens, terrain) built on advanced simulation tech to create varied edge cases, generate millions of labeled episodes for scalable model learning, and accelerate development while reducing cost and safety risk. It supports natural language control in simulated scenarios, lets users bring their own robot models or choose from commercially available ones, and includes tools for collaboration, environment sharing, and training workflows via LuckyHub, helping developers push models toward real-world performance more efficiently.Starting Price: Free -
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Amazon SageMaker JumpStart
Amazon
Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can access built-in algorithms with pretrained models from model hubs, pretrained foundation models to help you perform tasks such as article summarization and image generation, and prebuilt solutions to solve common use cases. In addition, you can share ML artifacts, including ML models and notebooks, within your organization to accelerate ML model building and deployment. SageMaker JumpStart provides hundreds of built-in algorithms with pretrained models from model hubs, including TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV. You can also access built-in algorithms using the SageMaker Python SDK. Built-in algorithms cover common ML tasks, such as data classifications (image, text, tabular) and sentiment analysis. -
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Maverick Studio
Maverick
Maverick Studio is a GPU-accelerated desktop app where our proprietary simulator of the physics of light is harnessed with intuitive and interactive drag-and-drop tools. Import your 3D models or CAD data files, and render amazing photo-real shots or turntable presentations with the best quality and the least effort imaginable. A bridge plug-in for Rhinoceros is also available. With our brand new Rhino plug-in you can Send (and Update) your models to Maverick Studio with a single click, preserving the materials, lights, and camera work done in Maverick. Enjoy our comfortable and user-friendly workflow to illuminate your jewelry models and achieve photo-real results thanks to our gradient lights and our physically-correct materials. Not a Rhino user? Export from your modeling software to any of the many file formats that Maverick can import natively and enjoy dressing your geometry with materials and lighting to achieve photo-real shots effortlessly.Starting Price: €3999 per month -
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Ansys Discovery
Ansys
Ansys Discovery features the first simulation-driven design tool combining instant physics simulation, high-fidelity simulation and interactive geometry modeling in a single easy-to-use experience. By combining interactive modeling and multiple simulation capabilities in a first-of-its-kind product, Discovery allows you to answer critical design questions earlier in the design process. This upfront approach to simulation saves time and effort on prototyping as you explore multiple design concepts in real time with no need to wait for simulation results. Ansys Discovery answers critical design questions early in your process with speed and accuracy. Boost productivity and performance by eliminating long waits for simulation results. Discovery lets engineers focus on innovation and product performance. By answering critical design questions early in the process, thus decreasing engineer labor and physical prototyping costs, Ansys Discovery allows for a ROI boost across your organization. -
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CoppeliaSim
Coppelia Robotics
CoppeliaSim, developed by Coppelia Robotics, is a versatile and powerful robot simulation platform utilized for rapid algorithm development, factory automation simulations, fast prototyping and verification, robotics education, remote monitoring, safety double-checking, and digital twin creation. It features a distributed control architecture, allowing each object or model to be individually controlled via embedded scripts (Python or Lua), plugins (C/C++), remote API clients (Python, Lua, Java, MATLAB, Octave, C, C++, Rust), or custom solutions. The simulator supports five physics engines, MuJoCo, Bullet Physics, ODE, Newton, and Vortex Dynamics, for fast and customizable dynamics calculations, enabling realistic simulation of real-world physics and object interactions, including collision response, grasping, soft bodies, strings, ropes, and cloths. CoppeliaSim provides forward and inverse kinematics calculations for any type of mechanism.Starting Price: $2,380 per year -
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Ansys Fluent
Ansys
Ansys Fluent is the industry-leading fluid simulation software known for its advanced physics modeling capabilities and industry leading accuracy. Ansys Fluent gives you more time to innovate and optimize product performance. Trust your simulation results with a software that has been extensively validated across a wide range of applications. With Ansys Fluent, you can create advanced physics models and analyze a variety of fluids phenomena—all in a customizable and intuitive space. Accelerate your design cycle with this powerful fluid simulation software. Ansys Fluent contains the best-in class physics models and can accurately and efficiently solve large , complex models. Ansys Fluent unlocks new potentials for CFD analysis. A fluid simulation software with fast pre-processing and faster solve times to help you be the fastest to break into the market. Fluent’s industry leading features enable limitless innovation, while never making a compromise on accuracy. -
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NeuroIntelligence
ALYUDA
NeuroIntelligence is a neural networks software application designed to assist neural network, data mining, pattern recognition, and predictive modeling experts in solving real-world problems. NeuroIntelligence features only proven neural network modeling algorithms and neural net techniques; software is fast and easy-to-use. Visualized architecture search, neural network training and testing. Neural network architecture search, fitness bars, network training graphs comparison. Training graphs, dataset error, network error, weights and errors distribution, neural network input importance. Testing, actual vs. output graph, scatter plot, response graph, ROC curve, confusion matrix. The interface of NeuroIntelligence is optimized to solve data mining, forecasting, classification and pattern recognition problems. You can create a better solution much faster using the tool's easy-to-use GUI and unique time-saving capabilities.Starting Price: $497 per user -
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samadii/em
Metariver Technology Co.,Ltd
samadii/em (electromagnetic simulation software) analyzes the electromagnetic field in 3d space using the Maxwell equation and using the FEM method. samadii/em (Electromagnetics Simulation) provides a multi-physics approach and high-performance electromagnetics simulation, with Samadii you can quickly address problems from semiconductors and displays to wireless communications, etc. With samadii/em you can analyze problems in electrostatic fields, transient electromagnetics, AC electromagnetic fields, magnetostatics as well as induction electronics, including the low-frequency and high-frequency ranges. -
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HyperWorks
Altair Engineering
HyperWorks provides easy-to-learn, effective workflows that leverage domain knowledge and increase team productivity, enabling the efficient development of today’s increasingly complex and connected products. The new HyperWorks experience was created to free engineers to move from physics to physics, domain to domain, and even create reports without ever leaving their model. Create, explore and optimize designs within HyperWorks to produce robust designs that accurately model structures, mechanisms, fluids, electromagnetics, electrical, embedded software, systems design, and manufacturing processes. The solution-specific workflows enhance a growing number of engineering processes including fatigue analysis, concept design optimization, CFD modeling, and design exploration. Each provides a meticulously designed and intuitive user interface, differentiated for each user profile while remaining consistent and easy to learn. -
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NVIDIA DIGITS
NVIDIA DIGITS
The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. DIGITS is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging. Interactively train models using TensorFlow and visualize model architecture using TensorBoard. Integrate custom plug-ins for importing special data formats such as DICOM used in medical imaging. -
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TorchMetrics
TorchMetrics
TorchMetrics is a collection of 90+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. A standardized interface to increase reproducibility. It reduces boilerplate. distributed-training compatible. It has been rigorously tested. Automatic accumulation over batches. Automatic synchronization between multiple devices. You can use TorchMetrics in any PyTorch model, or within PyTorch Lightning to enjoy additional benefits. Your data will always be placed on the same device as your metrics. You can log Metric objects directly in Lightning to reduce even more boilerplate. Similar to torch.nn, most metrics have both a class-based and a functional version. The functional versions implement the basic operations required for computing each metric. They are simple python functions that as input take torch.tensors and return the corresponding metric as a torch.tensor. Nearly all functional metrics have a corresponding class-based metric.Starting Price: Free -
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Torch
Torch
Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community. At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner. -
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NVIDIA Picasso
NVIDIA
NVIDIA Picasso is a cloud service for building generative AI–powered visual applications. Enterprises, software creators, and service providers can run inference on their models, train NVIDIA Edify foundation models on proprietary data, or start from pre-trained models to generate image, video, and 3D content from text prompts. Picasso service is fully optimized for GPUs and streamlines training, optimization, and inference on NVIDIA DGX Cloud. Organizations and developers can train NVIDIA’s Edify models on their proprietary data or get started with models pre-trained with our premier partners. Expert denoising network to generate photorealistic 4K images. Temporal layers and novel video denoiser generate high-fidelity videos with temporal consistency. A novel optimization framework for generating 3D objects and meshes with high-quality geometry. Cloud service for building and deploying generative AI-powered image, video, and 3D applications. -
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ThirdAI
ThirdAI
ThirdAI (pronunciation: /THərd ī/ Third eye) is a cutting-edge Artificial intelligence startup carving scalable and sustainable AI. ThirdAI accelerator builds hash-based processing algorithms for training and inference with neural networks. The technology is a result of 10 years of innovation in finding efficient (beyond tensor) mathematics for deep learning. Our algorithmic innovation has demonstrated how we can make Commodity x86 CPUs 15x or faster than most potent NVIDIA GPUs for training large neural networks. The demonstration has shaken the common knowledge prevailing in the AI community that specialized processors like GPUs are significantly superior to CPUs for training neural networks. Our innovation would not only benefit current AI training by shifting to lower-cost CPUs, but it should also allow the “unlocking” of AI training workloads on GPUs that were not previously feasible. -
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NVIDIA BioNeMo
NVIDIA
BioNeMo is an AI-powered drug discovery cloud service and framework built on NVIDIA NeMo Megatron for training and deploying large biomolecular transformer AI models at a supercomputing scale. The service includes pre-trained large language models (LLMs) and native support for common file formats for proteins, DNA, RNA, and chemistry, providing data loaders for SMILES for molecular structures and FASTA for amino acid and nucleotide sequences. The BioNeMo framework will also be available for download for running on your own infrastructure. ESM-1, based on Meta AI’s state-of-the-art ESM-1b, and ProtT5 are transformer-based protein language models that can be used to generate learned embeddings for tasks like protein structure and property prediction. OpenFold, a deep learning model for 3D structure prediction of novel protein sequences, will be available in BioNeMo service. -
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NVIDIA Triton™ inference server delivers fast and scalable AI in production. Open-source inference serving software, Triton inference server streamlines AI inference by enabling teams deploy trained AI models from any framework (TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, custom and more on any GPU- or CPU-based infrastructure (cloud, data center, or edge). Triton runs models concurrently on GPUs to maximize throughput and utilization, supports x86 and ARM CPU-based inferencing, and offers features like dynamic batching, model analyzer, model ensemble, and audio streaming. Triton helps developers deliver high-performance inference aTriton integrates with Kubernetes for orchestration and scaling, exports Prometheus metrics for monitoring, supports live model updates, and can be used in all major public cloud machine learning (ML) and managed Kubernetes platforms. Triton helps standardize model deployment in production.Starting Price: Free
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Evo 2
Arc Institute
Evo 2 is a genomic foundation model capable of generalist prediction and design tasks across DNA, RNA, and proteins. It utilizes a frontier deep learning architecture to model biological sequences at single-nucleotide resolution, achieving near-linear scaling of compute and memory relative to context length. Trained with 40 billion parameters and a 1 megabase context length, Evo 2 processes over 9 trillion nucleotides from diverse eukaryotic and prokaryotic genomes. This extensive training enables Evo 2 to perform zero-shot function prediction across multiple biological modalities, including DNA, RNA, and proteins, and to generate novel sequences with plausible genomic architecture. The model's capabilities have been demonstrated in tasks such as designing functional CRISPR systems and predicting disease-causing mutations in human genes. Evo 2 is publicly accessible via Arc's GitHub repository and is integrated into the NVIDIA BioNeMo framework. -
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Microsoft Cognitive Toolkit
Microsoft
The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK can be included as a library in your Python, C#, or C++ programs, or used as a standalone machine-learning tool through its own model description language (BrainScript). In addition you can use the CNTK model evaluation functionality from your Java programs. CNTK supports 64-bit Linux or 64-bit Windows operating systems. To install you can either choose pre-compiled binary packages, or compile the toolkit from the source provided in GitHub. -
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SIMULIA
Dassault Systèmes
Powered by the 3DEXPERIENCE® platform, SIMULIA delivers realistic simulation applications that enable users to reveal the world we live in. SIMULIA applications accelerate the process of evaluating the performance, reliability and safety of materials and products before committing to physical prototypes. Delivers powerful simulation of structures, fluids, multibody, and electromagnetics scenarios including complex assemblies directly linked with the product data. Modeling, simulation, and visualization technology are fully integrated on the 3DEXPERIENCE Platform, including process capture, publication, and re-use. The value of the customer’s existing investment in simulation horsepower is maximized by allowing simulation data, results, and IP to connect to the platform and become true corporate assets that powers innovation for all users. -
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Zebra by Mipsology
Mipsology
Zebra by Mipsology is the ideal Deep Learning compute engine for neural network inference. Zebra seamlessly replaces or complements CPUs/GPUs, allowing any neural network to compute faster, with lower power consumption, at a lower cost. Zebra deploys swiftly, seamlessly, and painlessly without knowledge of underlying hardware technology, use of specific compilation tools, or changes to the neural network, the training, the framework, and the application. Zebra computes neural networks at world-class speed, setting a new standard for performance. Zebra runs on highest-throughput boards all the way to the smallest boards. The scaling provides the required throughput, in data centers, at the edge, or in the cloud. Zebra accelerates any neural network, including user-defined neural networks. Zebra processes the same CPU/GPU-based trained neural network with the same accuracy without any change. -
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Geminai
YouNeed3D
Geminai is YN3D’s platform for delivering the most photorealistic Digital Twin that improves how all stakeholders operate, manage and service physical assets. Through visualizing the physical asset via reality models and connecting an asset’s information, Geminai enables users to centralize, visualize and share information in the most realistic and ‘true’ visual environment possible. Delivering a Digital Twin of your Assets via a True photorealistic representation offers the best insights available that will boost productivity and safety, reduce costs over the asset's lifecycle and allow all stakeholders to make more informed decisions when necessary. Remove interpretation errors and improve stakeholder collaboration by viewing assets with the highest-fidelity 3D models and not solely through 2D documents. -
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CUDA
NVIDIA
CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs. In GPU-accelerated applications, the sequential part of the workload runs on the CPU – which is optimized for single-threaded performance – while the compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers program in popular languages such as C, C++, Fortran, Python and MATLAB and express parallelism through extensions in the form of a few basic keywords. The CUDA Toolkit from NVIDIA provides everything you need to develop GPU-accelerated applications. The CUDA Toolkit includes GPU-accelerated libraries, a compiler, development tools and the CUDA runtime.Starting Price: Free -
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Neural Designer
Artelnics
Neural Designer is a powerful software tool for developing and deploying machine learning models. It provides a user-friendly interface that allows users to build, train, and evaluate neural networks without requiring extensive programming knowledge. With a wide range of features and algorithms, Neural Designer simplifies the entire machine learning workflow, from data preprocessing to model optimization. In addition, it supports various data types, including numerical, categorical, and text, making it versatile for domains. Additionally, Neural Designer offers automatic model selection and hyperparameter optimization, enabling users to find the best model for their data with minimal effort. Finally, its intuitive visualizations and comprehensive reports facilitate interpreting and understanding the model's performance.Starting Price: $2495/year (per user) -
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MPCPy
MPCPy
MPCPy is a Python package that facilitates the testing and implementation of occupant-integrated model predictive control (MPC) for building systems. The package focuses on the use of data-driven, simplified physical or statistical models to predict building performance and optimize control. Four main modules contain object classes to import data, interact with real or emulated systems, estimate and validate data-driven models, and optimize control input. While MPCPy provides an integration platform, it relies on free, open-source, third-party software packages for model implementation, simulators, parameter estimation algorithms, and optimization solvers. This includes Python packages for scripting and data manipulation as well as other more comprehensive software packages for specific purposes. In particular, modeling and optimization for physical systems currently rely on the Modelica language specification.Starting Price: Free -
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NVIDIA Cosmos
NVIDIA
NVIDIA Cosmos is a developer-first platform of state-of-the-art generative World Foundation Models (WFMs), advanced video tokenizers, guardrails, and an accelerated data processing and curation pipeline designed to supercharge physical AI development. It enables developers working on autonomous vehicles, robotics, and video analytics AI agents to generate photorealistic, physics-aware synthetic video data, trained on an immense dataset including 20 million hours of real-world and simulated video, to rapidly simulate future scenarios, train world models, and fine‑tune custom behaviors. It includes three core WFM types; Cosmos Predict, capable of generating up to 30 seconds of continuous video from multimodal inputs; Cosmos Transfer, which adapts simulations across environments and lighting for versatile domain augmentation; and Cosmos Reason, a vision-language model that applies structured reasoning to interpret spatial-temporal data for planning and decision-making.Starting Price: Free -
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SiMa
SiMa
SiMa offers a software-centric, embedded edge machine learning system-on-chip (MLSoC) platform that delivers high-performance, low-power AI solutions for various applications. The MLSoC integrates multiple modalities, including text, image, audio, video, and haptic inputs, performing complex ML inference and presenting outputs in any modality. It supports a wide range of frameworks (e.g., TensorFlow, PyTorch, ONNX) and can compile over 250 models, providing customers with an effortless experience and world-class performance-per-watt results. Complementing the hardware, SiMa.ai is designed for complete ML stack application development. It supports any ML workflow customers plan to deploy on the edge without compromising performance and ease of use. Palette's integrated ML compiler accepts any model from any neural network framework. -
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NVIDIA Isaac Lab
NVIDIA
NVIDIA Isaac Lab is a GPU‑accelerated, open source robot learning framework built on top of Isaac Sim, designed to unify and simplify robotics research workflows such as reinforcement learning, imitation learning, and motion planning. It leverages realistic sensor and physics simulation to support accurate training of embodied agents, providing ready‑to‑use environments, spanning manipulators, quadrupeds, and humanoids—with support for 30+ benchmark tasks and integration with popular RL libraries like RL Games, Stable Baselines, RSL RL, and SKRL. Isaac Lab features a modular, configuration‑driven design that enables developers to easily create, modify, and scale learning environments; it also supports collecting demonstrations via peripherals (gamepads, keyboards) and allows custom actuator models to facilitate sim‑to‑real transfer. The framework is built for both local and cloud deployment, accommodating flexible scaling of compute resources.Starting Price: Free -
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Fidelity CFD
Cadence Design Systems
Accelerate engineering with the industry's only intuitive, comprehensive CFD platform for multidisciplinary design and optimization. Computational fluid dynamics (CFD) is an aspect of multiphysics system analysis that simulates the behavior of fluids and their thermodynamic properties using numerical models. Engineers use the Cadence Fidelity CFD platform design processes, such as propulsion, aerodynamics, hydrodynamics, and combustion, to improve and increase the efficiency of products by reducing time-consuming and expensive physical testing. Fidelity CFD platform provides an easy-to-use end-to-end CFD solution for multidisciplinary design and optimization, in applications such as aerospace, automotive, turbomachinery, and marine industries. The platform, with its streamlined workflows, massively parallel architecture, and state-of-the-art solver technology, provides unprecedented performance and accuracy and increases engineering efficiency for today’s design challenges. -
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Piper TTS
Rhasspy
Piper is a fast, local neural text-to-speech (TTS) system optimized for devices like the Raspberry Pi 4, designed to deliver high-quality speech synthesis without relying on cloud services. It utilizes neural network models trained with VITS and exported to ONNX Runtime, enabling efficient and natural-sounding speech generation. Piper supports a wide range of languages, including English (US and UK), Spanish (Spain and Mexico), French, German, and many others, with voices available for download. Users can run Piper via the command line or integrate it into Python applications using the piper-tts package. The system allows for real-time audio streaming, JSON input for batch processing, and supports multi-speaker models. Piper relies on espeak-ng for phoneme generation, converting text into phonemes before synthesizing speech. It is employed in various projects such as Home Assistant, Rhasspy 3, NVDA, and others.Starting Price: Free -
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GigaChat
Sberbank
GigaChat knows how to answer user questions, maintain a dialogue, write program code, create texts and pictures based on descriptions within a single context. Unlike a foreign neural network, the GigaChat service initially already supports multimodal interaction and communicates more competently in Russian. The architecture of the GigaChat service is based on the neural network ensemble of the NeONKA (NEural Omnimodal Network with Knowledge-Awareness) model, which includes various neural network models and the method of supervised fine-tuning, reinforcement learning with human feedback. Thanks to this, Sber's new neural network can solve many intellectual tasks: keep up a conversation, write texts, answer factual questions. And the inclusion of the Kandinsky 2.1 model in the ensemble gives the neural network the skill of creating images. -
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MapleSim
Waterloo Maple
From digital twins for virtual commissioning to system-level models for complex engineering design projects, MapleSim is an advanced modeling tool that helps you reduce development time, lower costs, and diagnose real-world performance issues. Remove vibrations with better control code, not hardware upgrades. Diagnose root-cause performance issues with detailed simulation results. Validate new design performance before physical prototyping. MapleSim is an advanced system-level modeling and simulation tool that applies modern techniques to dramatically reduce model development time, provide greater insight into system behavior, and produce fast, high-fidelity simulations. Scale and connect as the needs of your simulations grow more complex. Take your designs further with our flexible modeling language. Combine components across different domains in a virtual prototype. Solve tough machine performance problems. -
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Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have contributed to the popularity of deep learning by reducing the effort and skills needed to design, train, and use deep learning models. Fabric for Deep Learning (FfDL, pronounced “fiddle”) provides a consistent way to run these deep-learning frameworks as a service on Kubernetes. The FfDL platform uses a microservices architecture to reduce coupling between components, keep each component simple and as stateless as possible, isolate component failures, and allow each component to be developed, tested, deployed, scaled, and upgraded independently. Leveraging the power of Kubernetes, FfDL provides a scalable, resilient, and fault-tolerant deep-learning framework. The platform uses a distribution and orchestration layer that facilitates learning from a large amount of data in a reasonable amount of time across compute nodes.
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Ansys Lumerical Multiphysics is a photonics component simulation software that enables the seamless design of photonic components by capturing multiphysics effects, including optical, thermal, electrical, and quantum well interactions, within a unified design environment. Tailored for design engineering workflows, this intuitive product design software offers a fast user experience, facilitating rapid design exploration and providing detailed insights into real-world product performance. It combines live physics and accurate high-fidelity simulation into an easy-to-use interface, supporting faster time-to-market. Key features include a finite element design environment, integrated multiphysics workflows, comprehensive material models, and capabilities for automation and optimization. The suite of solvers and seamless workflows in Lumerical Multiphysics accurately capture the interplay of physical effects in modeling both passive and active photonic components.
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LuxCoreRender
LuxCoreRender
LuxCoreRender is a physically based and unbiased rendering engine. Based on state of the art algorithms, LuxCoreRender simulates the flow of light according to physical equations, thus producing realistic images of photographic quality. LuxCoreRender is built on physically based equations that model the transportation of light. LuxCoreRender uses OpenCL to run on any number of CPUs and/or GPUs available. LuxCoreRender is and will always be free software, both for private and commercial use. LuxCoreRender is a physically based and unbiased rendering engine based on state of the art algorithms. A showcase of what LuxCoreRender users have been able to achieve. LuxCoreRender features a variety of material types. Apart from generic materials such as matte and glossy, physically accurate representations of metal, glass, and car paint are present. LuxCoreRender supports dynamic and interactive scene editing. -
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Gazebo
Gazebo
Gazebo is an open source robotics simulator that provides high-fidelity physics, rendering, and sensor models for developing and testing robot applications. It supports multiple physics engines, including ODE, Bullet, and Simbody, enabling accurate dynamics simulation. Gazebo offers advanced 3D graphics through rendering engines like OGRE v2, delivering realistic environments with high-quality lighting, shadows, and textures. It includes a wide array of sensors, such as laser range finders, 2D/3D cameras, IMUs, GPS, and more, with the ability to simulate sensor noise. Users can develop custom plugins for robot, sensor, and environment control, and interact with simulations via a plugin-based graphical interface powered by Gazebo GUI. Gazebo provides access to numerous robot models, including PR2, Pioneer2 DX, iRobot Create, and TurtleBot, and allows users to build new models using SDF.Starting Price: Free -
45
CodeT5
Salesforce
Code for CodeT5, a new code-aware pre-trained encoder-decoder model. Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. This is the official PyTorch implementation for the EMNLP 2021 paper from Salesforce Research. CodeT5-large-ntp-py is specially optimized for Python code generation tasks and employed as the foundation model for our CodeRL, yielding new SOTA results on the APPS Python competition-level program synthesis benchmark. This repo provides the code for reproducing the experiments in CodeT5. CodeT5 is a new pre-trained encoder-decoder model for programming languages, which is pre-trained on 8.35M functions in 8 programming languages (Python, Java, JavaScript, PHP, Ruby, Go, C, and C#). In total, it achieves state-of-the-art results on 14 sub-tasks in a code intelligence benchmark - CodeXGLUE. Generate code based on the natural language description. -
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Simcenter MAGNET
Siemens
Simcenter MAGNET is a powerful electromagnetic field simulation solution for performance prediction of motors, generators, sensors, transformers, actuators, solenoids, or any component with permanent magnets or coils. Simcenter MAGNET helps you predict the performance of any component with permanent magnets or coils. Perform low-frequency electromagnetic field simulations. Simcenter MAGNET includes capabilities to accurately model the physics of electromagnetic devices. This includes the ability to model manufacturing processes, temperature-dependent material properties, magnetization and de-magnetization modeling, and vector hysteresis models among others. Simcenter MAGNET also has a built-in motion solver with a six-degree-of-freedom capability. It allows for complex problems like magnetic levitation or complex motion to be accurately modeled and analyzed. This is made possible with a unique smart re-meshing technology. -
47
AWS Neuron
Amazon Web Services
It supports high-performance training on AWS Trainium-based Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances. For model deployment, it supports high-performance and low-latency inference on AWS Inferentia-based Amazon EC2 Inf1 instances and AWS Inferentia2-based Amazon EC2 Inf2 instances. With Neuron, you can use popular frameworks, such as TensorFlow and PyTorch, and optimally train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal code changes and without tie-in to vendor-specific solutions. AWS Neuron SDK, which supports Inferentia and Trainium accelerators, is natively integrated with PyTorch and TensorFlow. This integration ensures that you can continue using your existing workflows in these popular frameworks and get started with only a few lines of code changes. For distributed model training, the Neuron SDK supports libraries, such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP). -
48
Blue Hexagon
Blue Hexagon
We’ve designed our real-time deep learning platform to deliver speed of detection, efficacy and coverage that sets a new standard for cyber defense. We train our neural networks with global threat data that we’ve curated carefully via threat repositories, dark web, our deployments and from partners. Just like layers of neural networks can recognize your image in photos, our proprietary architecture of neural networks can identify threats in both payloads and headers. Every day, Blue Hexagon Labs validates the accuracy of our models with new threats in the wild. Our neural networks can identify a wide range of threats — file and fileless malware, exploits, C2 communications, malicious domains across Windows, Android, Linux platforms. Deep learning is a subset of machine learning that uses multi-layered artificial neural networks to learn data representation. -
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Options for every business to train deep learning and machine learning models cost-effectively. AI accelerators for every use case, from low-cost inference to high-performance training. Simple to get started with a range of services for development and deployment. Tensor Processing Units (TPUs) are custom-built ASIC to train and execute deep neural networks. Train and run more powerful and accurate models cost-effectively with faster speed and scale. A range of NVIDIA GPUs to help with cost-effective inference or scale-up or scale-out training. Leverage RAPID and Spark with GPUs to execute deep learning. Run GPU workloads on Google Cloud where you have access to industry-leading storage, networking, and data analytics technologies. Access CPU platforms when you start a VM instance on Compute Engine. Compute Engine offers a range of both Intel and AMD processors for your VMs.
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50
Alitheon FeaturePrint
Alitheon
Every physical object is unique. Our technology can distinguish any solid object from even visually identical items based on the object itself – there is no need for serialization, bar codes, RFID, etc. It can be used for object identification and authentication at low cost with statistically perfect results. This new branch of machine vision is called FeaturePrint™. As each person has unique fingerprints, every physical object has unique surface characteristics that distinguish it from all other items. Once an object is registered, its FeaturePrint is stored securely in the cloud. At any time, the object’s identity can be confirmed using something as pervasive as a smartphone. Our patented technology uses advanced machine vision, neural networks, and deep learning to identify each object from its unique properties without dependence on RFID, bar codes or other proxies. The identification of the object is inherent in the object itself.
