Showing 3 open source projects for "dynamics"

View related business solutions
  • Application Monitoring That Won't Slow Your App Down Icon
    Application Monitoring That Won't Slow Your App Down

    AppSignal's Rust-based agent is lightweight and stable. Already running in thousands of production apps.

    Full APM with errors, performance, logs, and uptime monitoring. 99.999% uptime SLA on the platform itself.
    Start Free
  • MongoDB Atlas runs apps anywhere Icon
    MongoDB Atlas runs apps anywhere

    Deploy in 115+ regions with the modern database for every enterprise.

    MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
    Start Free
  • 1
    FairChem

    FairChem

    FAIR Chemistry's library of machine learning methods for chemistry

    ...Version 2 modernizes the stack with a cleaner core package and breaking changes relative to V1, focusing on simpler installs and a stable API surface for production and research. The centerpiece models (e.g., UMA variants) plug directly into the ASE ecosystem via a FAIRChem calculator, so users can run relaxations, molecular dynamics, spin-state energetics, and surface catalysis workflows with the same pretrained network by switching a task flag. Tasks span heterogeneous domains—catalysis (OC20-style), inorganic materials (OMat), molecules (OMol), MOFs (ODAC), and molecular crystals (OMC)—allowing one model family to serve many simulations. The README provides quick paths for pulling models (e.g., via Hugging Face access), then running energy/force predictions on GPU or CPU.
    Downloads: 7 This Week
    Last Update:
    See Project
  • 2
    Neural Tangents

    Neural Tangents

    Fast and Easy Infinite Neural Networks in Python

    ...It lets researchers define architectures from familiar building blocks—convolutions, pooling, residual connections, and nonlinearities—and obtain not only the finite network but also the corresponding Gaussian Process (GP) kernel of its infinite-width limit. With a single specification, you can compute NNGP and NTK kernels, perform exact GP inference, and study training dynamics analytically for infinitely wide networks. The library closely mirrors JAX’s stax API while extending it to return a kernel_fn alongside init_fn and apply_fn, enabling drop-in workflows for kernel computation. Kernel evaluation is highly optimized for speed and memory, and computations can be automatically distributed across accelerators with near-linear scaling.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 3
    char-rnn

    char-rnn

    Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN)

    ...The project is designed for experimentation, offering tunable settings for depth, hidden size, dropout, sequence length, and sampling temperature to control creativity and coherence. It is frequently used as a learning project for understanding sequence modeling, recurrent training dynamics, and the practical details of text generation.
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next
MongoDB Logo MongoDB