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Physics-informed machine learning
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Machine learning used to represent physics-based and/or engineering models
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Laser Powder Bed Fusion
TS-PIELM: Time-Stepping Physics-Informed Extreme Learning Machine Facilitates Soil Consolidation Analyses
BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
Solving engineering eigenvalue problems with neural networks using the Rayleigh quotient
Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data
Toward Physics-Informed Machine Learning for Data Center Operations: A Tropical Case Study
Scientific machine learning in Hydrology: a unified perspective
Fourier-Invertible Neural Encoder (FINE) for Homogeneous Flows
Reconstructing Physics-Informed Machine Learning for Traffic Flow Modeling: a Multi-Gradient Descent and Pareto Learning Approach
LaPON: A Lagrange's-mean-value-theorem-inspired operator network for solving PDEs and its application on NSE
Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers
Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis
FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design
A Statistical Evaluation of Indoor LoRaWAN Environment-Aware Propagation for 6G: MLR, ANOVA, and Residual Distribution Analysis
Breaking the Diffraction Barrier for Passive Sources: Parameter-Decoupled Superresolution Assisted by Physics-Informed Machine Learning
Safe Physics-Informed Machine Learning for Dynamics and Control
A Mechanism-Learning Deeply Coupled Model for Remote Sensing Retrieval of Global Land Surface Temperature
Transforming Future Data Center Operations and Management via Physical AI
Physics-informed Modularized Neural Network for Advanced Building Control by Deep Reinforcement Learning
Physics-informed machine learning for building performance simulation-A review of a nascent field
Physics-Informed Deep B-Spline Networks for Dynamical Systems
NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs
Further Exploration of Precise Binding Energies from Physics Informed Machine Learning and the Development of a Practical Ensemble Model
A Multimodal Physics-Informed Neural Network Approach for Mean Radiant Temperature Modeling
A physics-informed Bayesian optimization method for rapid development of electrical machines
An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation
Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models
A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems
Generalizable and Fast Surrogates: Model Predictive Control of Articulated Soft Robots using Physics-Informed Neural Networks
Understanding Generalization in Physics Informed Models through Affine Variety Dimensions
PDE-DKL: PDE-constrained deep kernel learning in high dimensionality
Empirical modeling and hybrid machine learning framework for nucleate pool boiling on microchannel structured surfaces
Physics-Informed Machine Learning for Efficient Reconfigurable Intelligent Surface Design
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