Physical Simulations

36 papers with code • 0 benchmarks • 9 datasets

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Latest papers with no code

Symmetric Basis Convolutions for Learning Lagrangian Fluid Mechanics

no code yet • 25 Mar 2024

Learning physical simulations has been an essential and central aspect of many recent research efforts in machine learning, particularly for Navier-Stokes-based fluid mechanics.

Urban Sound Propagation: a Benchmark for 1-Step Generative Modeling of Complex Physical Systems

no code yet • 16 Mar 2024

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)

no code yet • 3 Mar 2024

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

no code yet • 16 Feb 2024

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

no code yet • 17 Jan 2024

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

no code yet • 20 Nov 2023

The realism of digital avatars is crucial in enabling telepresence applications with self-expression and customization.

TANGO: Time-Reversal Latent GraphODE for Multi-Agent Dynamical Systems

no code yet • 10 Oct 2023

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

no code yet • 6 Oct 2023

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.

Multi-Resolution Active Learning of Fourier Neural Operators

no code yet • 29 Sep 2023

Fourier Neural Operator (FNO) is a popular operator learning framework, which not only achieves the state-of-the-art performance in many tasks, but also is highly efficient in training and prediction.

Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives

no code yet • NeurIPS 2023

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.