Papers Physics-informed machine learning
“Physics-informed machine learning” 태그가 달린 논문 192편 · 필터 해제
Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Laser Powder Bed Fusion
Efficient simulation of Laser Powder Bed Fusion (LPBF) is crucial for process prediction due to the lasting issue of high computation cost using traditional numerical methods such as finite element analysis (FEA). This s…
Physics-informed machine learningTransfer LearningTS-PIELM: Time-Stepping Physics-Informed Extreme Learning Machine Facilitates Soil Consolidation Analyses
Accuracy and efficiency of the conventional physics-informed neural network (PINN) need to be improved before it can be a competitive alternative for soil consolidation analyses. This paper aims to overcome these limitat…
Computational EfficiencyPhysics-informed machine learningSolving engineering eigenvalue problems with neural networks using the Rayleigh quotient
From characterizing the speed of a thermal system's response to computing natural modes of vibration, eigenvalue analysis is ubiquitous in engineering. In spite of this, eigenvalue problems have received relatively littl…
Physics-informed machine learningBridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
BridgeNet is a novel hybrid framework that integrates convolutional neural networks with physics-informed neural networks to efficiently solve non-linear, high-dimensional Fokker-Planck equations (FPEs). Traditional PINN…
Physics-informed machine learningToward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
Artificial intelligence (AI) is reshaping inverse design across manufacturing domain, enabling high-performance discovery in materials, products, and processes. However, purely data-driven approaches often struggle in re…
Physics-informed machine learningA Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data
In compressible fluid flow, reconstructing shocks, discontinuities, rarefactions, and their interactions from sparse measurements is an important inverse problem with practical applications. Moreover, physics-informed ma…
Physics-informed machine learningToward Physics-Informed Machine Learning for Data Center Operations: A Tropical Case Study
Data centers are the backbone of computing capacity. Operating data centers in the tropical regions faces unique challenges due to consistently high ambient temperature and elevated relative humidity throughout the year.…
Physics-informed machine learningScientific machine learning in Hydrology: a unified perspective
Scientific machine learning (SciML) provides a structured approach to integrating physical knowledge into data-driven modeling, offering significant potential for advancing hydrological research. In recent years, multipl…
Physics-informed machine learningFourier-Invertible Neural Encoder (FINE) for Homogeneous Flows
Invertible neural architectures have recently attracted attention for their compactness, interpretability, and information-preserving properties. In this work, we propose the Fourier-Invertible Neural Encoder (FINE), whi…
Dimensionality ReductionPhysics-informed machine learningRepresentation LearningReconstructing Physics-Informed Machine Learning for Traffic Flow Modeling: a Multi-Gradient Descent and Pareto Learning Approach
Physics-informed machine learning (PIML) is crucial in modern traffic flow modeling because it combines the benefits of both physics-based and data-driven approaches. In conventional PIML, physical information is typical…
Physics-informed machine learningLaPON: A Lagrange's-mean-value-theorem-inspired operator network for solving PDEs and its application on NSE
Accelerating the solution of nonlinear partial differential equations (PDEs) while maintaining accuracy at coarse spatiotemporal resolution remains a key challenge in scientific computing. Physics-informed machine learni…
Physics-informed machine learningWeather ForecastingBeyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers
Real-world systems, from aerospace to railway engineering, are modeled with partial differential equations (PDEs) describing the physics of the system. Estimating robust solutions for such problems is essential. Deep lea…
Operator learningPhysics-informed machine learningPotential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis
This study critically examines the performance of physics-informed machine learning (PIML) approaches for traffic flow modeling, defining the failure of a PIML model as the scenario where it underperforms both its purely…
Physics-informed machine learningFMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design
We propose FMEnets, a physics-informed machine learning framework for the design and analysis of non-ideal plug flow reactors. FMEnets integrates the fundamental governing equations (Navier-Stokes for fluid flow, materia…
Kolmogorov-Arnold NetworksPhysics-informed machine learningA Statistical Evaluation of Indoor LoRaWAN Environment-Aware Propagation for 6G: MLR, ANOVA, and Residual Distribution Analysis
Modeling path loss in indoor LoRaWAN technology deployments is inherently challenging due to structural obstructions, occupant density and activities, and fluctuating environmental conditions. This study proposes a two-s…
Hybrid Machine LearningIndoor Localizationinput filteringInterpretable Machine Learning+1Breaking the Diffraction Barrier for Passive Sources: Parameter-Decoupled Superresolution Assisted by Physics-Informed Machine Learning
We present a parameter-decoupled superresolution framework for estimating sub-wavelength separations of passive two-point sources without requiring prior knowledge or control of the source. Our theoretical foundation cir…
Physics-informed machine learningSafe Physics-Informed Machine Learning for Dynamics and Control
This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models and safety guarantees. As machine learn…
Autonomous VehiclesDecision MakingPhysics-informed machine learningUncertainty QuantificationA Mechanism-Learning Deeply Coupled Model for Remote Sensing Retrieval of Global Land Surface Temperature
Land surface temperature (LST) retrieval from remote sensing data is pivotal for analyzing climate processes and surface energy budgets. However, LST retrieval is an ill-posed inverse problem, which becomes particularly …
Physics-informed machine learningRetrievalTransforming Future Data Center Operations and Management via Physical AI
Data centers (DCs) as mission-critical infrastructures are pivotal in powering the growth of artificial intelligence (AI) and the digital economy. The evolution from Internet DC to AI DC has introduced new challenges in …
ManagementPhysics-informed machine learningPhysics-informed Modularized Neural Network for Advanced Building Control by Deep Reinforcement Learning
Physics-informed machine learning (PIML) provides a promising solution for building energy modeling and can serve as a virtual environment to enable reinforcement learning (RL) agents to interact and learn. However, chal…
Deep Reinforcement LearningPhysics-informed machine learningReinforcement Learning (RL)