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microsoft / QlibQlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.
EleutherAI / PythiaThe hub for EleutherAI's work on interpretability and learning dynamics
deepmodeling / Deepmd KitA deep learning package for many-body potential energy representation and molecular dynamics
google-research / PlanetLearning Latent Dynamics for Planning from Pixels
utiasDSL / Safe Control GymPyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
akanazawa / Human DynamicsProject for paper "Learning 3D Human Dynamics from Video"
Mohamedelrefaie / DrivAerNetA Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
kchua / Handful Of TrialsExperiment code for "Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models"
mit-biomimetics / FldRepository for our paper: FLD: Fourier Latent Dynamics for Structured Motion Representation and Learning, Proceedings of the 12th International Conference on Learning Representations (ICLR)
WillDreamer / Awesome AI4CFDRecent Advances on Machine Learning for Computational Fluid Dynamics: A Survey
sha256feng / Mldl Md DynamicsA repository of update in molecular dynamics field by recent progress in machine learning and deep learning.
pico-lm / Pico TrainA minimalistic framework for transparently training language models and storing comprehensive checkpoints for in-depth learning dynamics research.
loliverhennigh / Computational Fluid Dynamics Machine Learning ExamplesThis repo contains some tutorial type programs showing some basic ways machine learning can be applied to CFD.
xie-lab-ml / Deep Learning Dynamics Paper ListThis is a list of peer-reviewed representative papers on deep learning dynamics (optimization dynamics of neural networks). The success of deep learning attributes to both network architecture and stochastic optimization. Thus, deep learning dynamics play an essentially important role in theoretical foundation of deep learning.
AspirinCode / Awesome AI4MolConformation MDList of molecules (small molecules, RNA, peptide, protein, enzymes, antibody, and PPIs) conformations and molecular dynamics (force fields) using generative artificial intelligence and deep learning
ImanRHT / QECOA QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning (DRL) for Mobile Edge Computing (MEC) | This algorithm captures the dynamics of the MEC environment by integrating the Dueling Double Deep Q-Network (D3QN) model with Long Short-Term Memory (LSTM) networks.
AndreWeiner / Machine Learning Applied To CfdExamples of how to use machine learning algorithms in computational fluid dynamics.
luo-yining / CFDBenchA large-scale benchmark for machine learning methods in fluid dynamics
Gor-Ren / Gym JsbsimA reinforcement learning environment for aircraft control using the JSBSim flight dynamics model
thuml / KoopaCode release for "Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors" (NeurIPS 2023), https://arxiv.org/abs/2305.18803
