Search Results for "python q learning"

Brax is a fast and fully differentiable physics engine for large-scale rigid body simulations, built on JAX. It is designed for research in reinforcement learning and robotics, enabling efficient simulations and gradient-based optimization.

...The platform includes built-in interactive visualization using OpenGL and a native graphical interface for analyzing and testing simulations. Additionally, it offers extensive utility functions for physics computation, Python bindings for developers, and a Unity plug-in to enable integration with game engines and visualization tools.

Physical Symbolic Optimization (Φ-SO) - A symbolic optimization package built for physics. Symbolic regression module uses deep reinforcement learning to infer analytical physical laws that fit data points, searching in the space of functional forms.

GXSM -- Gnome X Scanning Microscopy: A multi-channel image and vector-probe data acquisition and visualization system designed for SPM techniques (STM,AFM..), but also SPA-LEED/LEED/LEEM data analysis. A plug-in interface allows any user add-on data-processing and special hardware and instrument support. Latest: NC-AFM and related explorative methods as SQDM can be configured. High-Speed external PAC-PLL hardware option with digital DSP link. Based on several hardware options it supports...

Data evaluation tool for the magnetism reflectometer at the spallation neutron source (BL-4A@SNS). Reads raw nexus files (HDF5) of histogrammed or event mode data to create reflectivity curves and 2D Q-maps.

Keywords (XNDiff): -SAXS -SANS -absolute units -core (double)shell crystalline nanoparticles -with a parallelepidal shape -particle assemblies -powder and ensemble average -C/C++ -Unix -OpenMP -HPC Cluster Keywords (BatchMultiFit): -simultaneous fits for several SAXS and SANS curves with simulation data from XNDiff -SANS data can be smeared with dq values from experimental data sets or analytical functions -Mathematica console -local and global optimizers (simulated annealing, differential evolution, Nelder-Mead, ...) can be used -range for fit parameters and further constraints between fit parameters -parallelized (typ. 4-8 threads) TODO (BatchMultiFit): -read and use errorbars from experimental data sets -allow different q-ranges for different data sets in the fits -rewrite and test in Python using e.g. the lmfit module: https://pypi.python.org/pypi/lmfit/ to get rid of Mathematica and to run it on HPC clusters