tailintalent / AI_physicistLinks
AI Physicist, a paradigm with algorithms for learning theories from data, by Wu and Tegmark (2019)
☆34Updated 4 years ago
Alternatives and similar repositories for AI_physicist
Users that are interested in AI_physicist are comparing it to the libraries listed below
Sorting:
- Bayesian algorithm execution (BAX)☆49Updated 3 years ago
- ☆30Updated 2 years ago
- predicting equations from raw data with deep learning☆56Updated 6 years ago
- LaTeX source code for the slides☆23Updated 3 years ago
- Prototypes of differentiable differential equation solvers in JAX.☆27Updated 5 years ago
- Learning with operator-valued kernels☆22Updated 2 years ago
- [DMLR] Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery☆33Updated last year
- Blazing fast symbolic regresison☆76Updated 5 years ago
- Code used for experiments in https://arxiv.org/abs/2008.08601☆19Updated 4 years ago
- Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning (NeurIPS 2020)☆22Updated 2 years ago
- Turning SymPy expressions into JAX functions☆45Updated 4 years ago
- Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen☆25Updated 5 years ago
- ☆72Updated 4 years ago
- Fast Function Extraction☆83Updated 8 months ago
- A variational method for fast, approximate inference for stochastic differential equations.☆44Updated 7 years ago
- Riemannian Optimization Using JAX☆49Updated last year
- Python bindings and scikit-learn interface for the Operon library for symbolic regression.☆53Updated 2 weeks ago
- PyTorch block-diagonal ODE CUDA solver, designed for gradient-based optimization☆16Updated 5 years ago
- Tree Approximate Message Passing☆30Updated last year
- ☆80Updated 3 years ago
- ☆51Updated 11 months ago
- A pytorch version of hamiltonian monte carlo☆14Updated 6 years ago
- Discontinuous Hamiltonian Monte Carlo in JAX☆41Updated 5 years ago
- Minimal Implementation of Bayesian Optimization in JAX☆95Updated 2 months ago
- Riemannian Convex Potential Maps☆67Updated 2 years ago
- Official repository for the paper "Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules" (…☆22Updated 2 weeks ago
- Examples of more involved applications using Geomstats☆32Updated 4 years ago
- Equation Learner, a neural network approach to symbolic regression☆79Updated 9 months ago
- Wraps PyTorch code in a JIT-compatible way for JAX. Supports automatically defining gradients for reverse-mode AutoDiff.☆54Updated last month
- A library for random feature maps in Python.☆16Updated 4 years ago