Physical Simulations
38 papers with code • 0 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in Physical Simulations
Datasets
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ABC Dataset
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PlasticineLab
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CAMELS Multifield Dataset
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ClimART
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Expressive Gaussian mixture models for high-dimensional statistical modelling: simulated data and neural network model files
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A Simulated 4-DOF Ship Motion Dataset for System Identification under Environmental Disturbances
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2D_NACA_RANS
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Workshop Tools Dataset
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DrivAerNet
Latest papers with no code
Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer
Humanoid-Gym is an easy-to-use reinforcement learning (RL) framework based on Nvidia Isaac Gym, designed to train locomotion skills for humanoid robots, emphasizing zero-shot transfer from simulation to the real-world environment.
Urban Sound Propagation: a Benchmark for 1-Step Generative Modeling of Complex Physical Systems
Data-driven modeling of complex physical systems is receiving a growing amount of attention in the simulation and machine learning communities.
ML4PhySim : Machine Learning for Physical Simulations Challenge (The airfoil design)
The aim of this competition is to encourage the development of new ML techniques to solve physical problems using a unified evaluation framework proposed recently, called Learning Industrial Physical Simulations (LIPS).
Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations
The protein dynamics are common and important for their biological functions and properties, the study of which usually involves time-consuming molecular dynamics (MD) simulations in silico.
Space and Time Continuous Physics Simulation From Partial Observations
Our practical implementation involves recurrent GNNs and a spatio-temporal attention observer capable of interpolating the solution at arbitrary locations.
DiffAvatar: Simulation-Ready Garment Optimization with Differentiable Simulation
By integrating physical simulation into the optimization loop and accounting for the complex nonlinear behavior of cloth and its intricate interaction with the body, our framework recovers body and garment geometry and extracts important material parameters in a physically plausible way.
TANGO: Time-Reversal Latent GraphODE for Multi-Agent Dynamical Systems
Learning complex multi-agent system dynamics from data is crucial across many domains, such as in physical simulations and material modeling.
Deep learning modelling of manufacturing and build variations on multi-stage axial compressors aerodynamics
Application of deep learning methods to physical simulations such as CFD (Computational Fluid Dynamics) for turbomachinery applications, have been so far of limited industrial relevance.
Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives
We compare our approach to the state of the art on diverse scenes from DTU, and demonstrate its robustness on real-life captures from BlendedMVS and Nerfstudio.
Generalizing Graph ODE for Learning Complex System Dynamics across Environments
In practice, however, we might observe multiple systems that are generated across different environments, which differ in latent exogenous factors such as temperature and gravity.
