Papers with Code 아카이브

Physical Simulations

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

5

보존된 평가 기록

16

코드

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

https://github.com/ArashRabbani/DeePore

공식 코드

DeePore: a deep learning workflow for rapid and comprehensive characterization of porous materials

https://github.com/Dolorousrtur/HOOD

공식 코드

HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics

https://github.com/Garoe/bath-fire-shader

공식 코드

Physics-driven Fire Modeling from Multi-view Images

https://github.com/Hananel-Hazan/bindsnet

공식 코드

BindsNET: A machine learning-oriented spiking neural networks library in Python

https://github.com/HugoBA92/PBNS

공식 코드

PBNS: Physically Based Neural Simulator for Unsupervised Garment Pose Space Deformation

https://github.com/MMehdiMousavi/SuperCaustics

공식 코드

SuperCaustics: Real-time, open-source simulation of transparent objects for deep learning applications

https://github.com/MartinHahner/LiDAR_fog_sim

공식 코드

Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather

https://github.com/OPTML-Group/DeepZero

공식 코드

DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training

https://github.com/RolnickLab/climart

공식 코드

ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models

https://github.com/YulhwaKim/RRAMScalable_BNN

공식 코드

Neural Network-Hardware Co-design for Scalable RRAM-based BNN Accelerators

https://github.com/automl/CARL

공식 코드

CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning

https://github.com/bjourne/fast_snn_fpga

공식 코드

Fast Algorithms for Spiking Neural Network Simulation with FPGAs

https://github.com/borbysh/coolmomentum

공식 코드

CoolMomentum: A Method for Stochastic Optimization by Langevin Dynamics with Simulated Annealing

https://github.com/ctlee/gamer

공식 코드

3D mesh processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries

https://github.com/diogodcarvalho/gns-sheet-model

공식 코드

Learning the dynamics of a one-dimensional plasma model with graph neural networks

https://github.com/eydcao/bsms-gnn

공식 코드

Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN

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

공식 코드

Neural General Circulation Models for Weather and Climate

https://github.com/hachanook/pyinn

공식 코드

Interpolating neural network: A novel unification of machine learning and interpolation theory

https://github.com/hbertiche/NeuralClothSim

공식 코드

Neural Cloth Simulation

https://github.com/jlinki/ggns

공식 코드

Grounding Graph Network Simulators using Physical Sensor Observations

https://github.com/junchengli1/Sim-Suction-API

공식 코드

Sim-Suction: Learning a Suction Grasp Policy for Cluttered Environments Using a Synthetic Benchmark

https://github.com/l-yezhu/astrodsb

공식 코드

Dynamic Diffusion Schrödinger Bridge in Astrophysical Observational Inversions

https://github.com/llnl/gplasdi

공식 코드

Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical Simulations

https://github.com/lmb-freiburg/tree-planting

공식 코드

Climate-sensitive Urban Planning through Optimization of Tree Placements

https://github.com/lychan110/metaset

공식 코드

METASET: Exploring Shape and Property Spaces for Data-Driven Metamaterials Design

https://github.com/maxxxzdn/erwin

공식 코드

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

https://github.com/mgolbabaee/LRTV-MRFResnet-for-MRFingerprinting

공식 코드

Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders

https://github.com/naufalso/adversarial-manhole

공식 코드

Adversarial Manhole: Challenging Monocular Depth Estimation and Semantic Segmentation Models with Patch Attack

https://github.com/navamikairanda/phi_sft

공식 코드

φ-SfT: Shape-from-Template with a Physics-Based Deformation Model

https://github.com/npallikarakis/ML_inv_eigenvalue

공식 코드

Application of machine learning regression models to inverse eigenvalue problems

https://github.com/poltimmer/CGNE

공식 코드

Efficient Probabilistic Modeling of Crystallization at Mesoscopic Scale

https://github.com/roboterax/humanoid-gym

공식 코드

Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer

https://github.com/sciann/sciann-applications

공식 코드

Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training

https://github.com/shib0li/mra-fno

공식 코드

Multi-Resolution Active Learning of Fourier Neural Operators

https://github.com/syscv/lidar_snow_sim

공식 코드

LiDAR Snowfall Simulation for Robust 3D Object Detection

https://github.com/taichi-dev/taichi

공식 코드

DiffTaichi: Differentiable Programming for Physical Simulation

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