Papers with Code 아카이브

Physics-informed machine learning

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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/nahshonmokua/LoRaWAN-IndoorPathLossModeling-ML/blob/main/MLR%20(ANOVA%20%26%20SHADOW%20ANALYSIS).ipynb

공식 코드

A Statistical Evaluation of Indoor LoRaWAN Environment-Aware Propagation for 6G: MLR, ANOVA, and Residual Distribution Analysis

https://github.com/nathandoumeche/piml_as_a_kernel_method

공식 코드

Physics-informed machine learning as a kernel method

https://github.com/orchardlanl/dpfehm.jl

공식 코드

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management

https://github.com/osu-complex-systems/mis-me

공식 코드

MIS-ME: A Multi-modal Framework for Soil Moisture Estimation

https://github.com/peterdsharpe/neuralfoil

공식 코드

NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning

https://github.com/pnnl/neuromancer

공식 코드

Differentiable Predictive Control for Large-Scale Urban Road Networks

https://github.com/rbischof/relative_balancing

공식 코드

Multi-Objective Loss Balancing for Physics-Informed Deep Learning

https://github.com/rezanajian/fol

공식 코드

Finite Operator Learning: Bridging Neural Operators and Numerical Methods for Efficient Parametric Solution and Optimization of PDEs

https://github.com/saadzia10/Physics-Encoded-ResNet

공식 코드

Physics Encoded Blocks in Residual Neural Network Architectures for Digital Twin Models

https://github.com/srigas/jaxKAN

공식 코드

Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold Networks

https://github.com/stfc-sciml/zerocoordinateshift

공식 코드

Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator Learning

https://github.com/swn-group-at-tu-berlin/lila-pinn

공식 코드

Estimating irregular water demands with physics-informed machine learning to inform leakage detection

https://github.com/taniyakapoor/AAAI24_Generalization_PIML

공식 코드

Neural oscillators for generalization of physics-informed machine learning

https://github.com/terencetaothucb/TBSI-Sunwoda-Battery-Dataset

공식 코드

Non-destructive Degradation Pattern Decoupling for Ultra-early Battery Prototype Verification Using Physics-informed Machine Learning

https://github.com/tsinghua-fib-lab/spdiff

공식 코드

Social Physics Informed Diffusion Model for Crowd Simulation

https://github.com/yeungyh/pickle

공식 코드

Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problems

https://github.com/zlaidyn/neural-modal-ode-demo

공식 코드

Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures

https://github.com/zykhoo/separablenns

공식 코드

Separable Hamiltonian Neural Networks

https://github.com/ConstitutiveANN/vCANN

Viscoelastic Constitutive Artificial Neural Networks (vCANNs) $-$ a framework for data-driven anisotropic nonlinear finite viscoelasticity

https://github.com/Jonas-Nicodemus/PINNs-based-MPC

Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators

https://github.com/PaddlePaddle/PaddleScience

Multi-Objective Loss Balancing for Physics-Informed Deep Learning

https://github.com/Willcox-Research-Group/operator-inference

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

https://github.com/ZongrenZou/MHPINN

L-HYDRA: Multi-Head Physics-Informed Neural Networks

https://github.com/elizqian/operator-inference

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

https://github.com/elizqian/transform-and-learn

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

https://github.com/georgiosdb/PINN-COVID-19

Physics-informed machine learning for the COVID-19 pandemic: Adherence to social distancing and short-term predictions for eight countries

https://github.com/i207m/pinnacle

Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications

https://github.com/pehersto/reproj

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

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