Papers with Code 아카이브

Physics-informed machine learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 0
보존된 평가 기록: 0

Machine learning used to represent physics-based and/or engineering models

코드

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

https://github.com/AbhilashMathews/PlasmaPINNtheory

공식 코드

Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experiment

https://github.com/Bostanabad-Research-Group/GP-for-pde-solving

공식 코드

A Gaussian Process Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations

https://github.com/Dibyakanti/Towards-Size-Independent-Generalization-Bounds-for-Deep-Operator-Nets

공식 코드

Towards Size-Independent Generalization Bounds for Deep Operator Nets

https://github.com/IntelligentMechanicsLab/NIM-Tutorial

공식 코드

Differentiable Neural-Integrated Meshfree Method for Forward and Inverse Modeling of Finite Strain Hyperelasticity

https://github.com/JRice15/physics-informed-autoencoders

공식 코드

Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forecasting

https://github.com/MachineLearningJournalClub/MLJC-UniTo-ProjectX-2020-public

공식 코드

Physics-Informed Machine Learning Simulator for Wildfire Propagation

https://github.com/NathanDoumeche/numerical_PIML_kernel

공식 코드

Physics-informed kernel learning

https://github.com/PML-UCF/pinn

공식 코드

Fleet Prognosis with Physics-informed Recurrent Neural Networks

https://github.com/RamiCassia/GraphGPS-for-Sparse-Reconstruction

공식 코드

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data

https://github.com/VhaoYan/PDE-constrained-DKL

공식 코드

PDE-DKL: PDE-constrained deep kernel learning in high dimensionality

https://github.com/WeihengZ/PI-DCON

공식 코드

Physics-informed Discretization-independent Deep Compositional Operator Network

https://github.com/adamrupe/emergent-organization

공식 코드

Unsupervised Discovery of Extreme Weather Events Using Universal Representations of Emergent Organization

https://github.com/afrah/QCPINN

공식 코드

QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs

https://github.com/ahmad-shirazi/DSSRNN

공식 코드

DSSRNN: Decomposition-Enhanced State-Space Recurrent Neural Network for Time-Series Analysis

https://github.com/ai-research-disi/ode-discovery-with-ude

공식 코드

An analysis of Universal Differential Equations for data-driven discovery of Ordinary Differential Equations

https://github.com/alanZee/lapon-light-open

공식 코드

LaPON: A Lagrange's-mean-value-theorem-inspired operator network for solving PDEs and its application on NSE

https://github.com/anmold-07/physics-guided-blackout-mitigation

공식 코드

RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors

https://github.com/citrineinformatics-erd-public/piml_glass_forming_ability

공식 코드

Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming ability

https://github.com/dayanbatuofu/value_appro_game

공식 코드

Value Approximation for Two-Player General-Sum Differential Games with State Constraints

https://github.com/dinhvietcuong1996/pinn-mosquito

공식 코드

Adapting Physics-Informed Neural Networks to Improve ODE Optimization in Mosquito Population Dynamics

https://github.com/echo-ji/stden

공식 코드

STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction

https://github.com/edisonylei/reconstructing-piml-through-mgda

공식 코드

Reconstructing Physics-Informed Machine Learning for Traffic Flow Modeling: a Multi-Gradient Descent and Pareto Learning Approach

https://github.com/felixriese/hyperspectral-processing

공식 코드

Fusion of hyperspectral and ground penetrating radar to estimate soil moisture

https://github.com/fnerrise/lnns_magnav

공식 코드

Physics-Informed Calibration of Aeromagnetic Compensation in Magnetic Navigation Systems using Liquid Time-Constant Networks

https://github.com/frazane/pcpp-workflow

공식 코드

Physics-constrained deep learning postprocessing of temperature and humidity

https://github.com/hipersimlab/pecann-ddm

공식 코드

Non-overlapping, Schwarz-type Domain Decomposition Method for Physics and Equality Constrained Artificial Neural Networks

https://github.com/idaholabresearch/bihnns

공식 코드

Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference

https://github.com/intel/tiny-dpcpp-nn

공식 코드

Fully-fused Multi-Layer Perceptrons on Intel Data Center GPUs

https://github.com/kleok/insar4sm

공식 코드

Soil moisture estimation from Sentinel-1 interferometric observations over arid regions

https://github.com/kuangdai/sofa

공식 코드

Deep Learning Evidence for Global Optimality of Gerver's Sofa

https://github.com/lmandl/separable-pi-deeponet

공식 코드

Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning

https://github.com/minhtriet/hydrogen_emb

공식 코드

A Machine Learning Pressure Emulator for Hydrogen Embrittlement

https://github.com/mpenwarden/knw-piml

공식 코드

Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning (PIML) Methods: Towards Robust Metrics

https://github.com/mpierzyna/piml

공식 코드

Π-ML: A dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layer

https://github.com/mwoodward-cpu/learningsph

공식 코드

Physics informed machine learning with Smoothed Particle Hydrodynamics: Hierarchy of reduced Lagrangian models of turbulence

https://github.com/nPIML-team/nPIML

공식 코드

Noise-aware Physics-informed Machine Learning for Robust PDE Discovery

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