Papers with Code 아카이브

Physical Simulations

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

3

평가표

5

보존된 평가 기록

16

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/thunil/Physics-Based-Deep-Learning

공식 코드

Physics-based Deep Learning

https://github.com/tum-pbs/PhiFlow

공식 코드

Physics-based Deep Learning

https://github.com/tum-pbs/diffusion-based-flow-prediction

공식 코드

Physics-based Deep Learning

https://github.com/tum-pbs/pbdl-book

공식 코드

Physics-based Deep Learning

https://github.com/tum-pbs/pbdl-dataset

공식 코드

Physics-based Deep Learning

https://github.com/tum-pbs/sfbc

공식 코드

Symmetric Basis Convolutions for Learning Lagrangian Fluid Mechanics

https://github.com/urban-sound-data/urban-sound-data

공식 코드

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

https://github.com/wenbowen123/catgrasp

공식 코드

CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation

https://github.com/xingyuansun/amorsyn

공식 코드

Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate

https://github.com/xitorch/xitorch

공식 코드

$ξ$-torch: differentiable scientific computing library

https://github.com/yellowshippo/isogcn-iclr2021

공식 코드

Isometric Transformation Invariant and Equivariant Graph Convolutional Networks

https://github.com/yuanming-hu/difftaichi

공식 코드

DiffTaichi: Differentiable Programming for Physical Simulation

https://github.com/yuyudeep/hcmt

공식 코드

Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer

https://github.com/zhaoyj21/fcg

공식 코드

Predicting Fatigue Crack Growth via Path Slicing and Re-Weighting

https://github.com/baturaysaglam/RIS-MISO-Deep-Reinforcement-Learning

Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning

https://github.com/britacatalin/bsa

Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems

https://github.com/google-research/dinosaur

Neural General Circulation Models for Weather and Climate

https://github.com/hongtaowu67/container_imagine

Is That a Chair? Imagining Affordances Using Simulations of an Articulated Human Body

https://github.com/lstrgar/eldir

DiffTaichi: Differentiable Programming for Physical Simulation

https://github.com/neuralgcm/neuralgcm

Neural General Circulation Models for Weather and Climate

https://github.com/thunil/Deep-Flow-Prediction

Physics-based Deep Learning

https://github.com/unrealcv/unrealcv

UnrealCV: Connecting Computer Vision to Unreal Engine

https://github.com/wanjiaZhao1203/TREAT

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

https://github.com/y-u-a-n-l-i/Climate_NeRF

ClimateNeRF: Extreme Weather Synthesis in Neural Radiance Field

https://github.com/yangyucheng000/Paper-4/tree/main/HOOD-ms

HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.