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Physics-guided, physics-informed, and physics-encoded neural networks and operators in scientific computing: Fluid and solid mechanics
Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …
learning and deep learning to push scientific computing forward in a range of disciplines …
Multifidelity graph neural networks for efficient and accurate mesh‐based partial differential equations surrogate modeling
Accurately predicting the dynamics of complex systems governed by partial differential
equations (PDEs) is crucial in various applications. Traditional numerical methods such as …
equations (PDEs) is crucial in various applications. Traditional numerical methods such as …
Multi-fidelity graph neural networks for efficient power flow analysis under high-dimensional demand and renewable generation uncertainty
The modernization of power systems faces uncertainties due to fluctuating renewable
energy sources, electric vehicle expansion, and demand response initiatives. These …
energy sources, electric vehicle expansion, and demand response initiatives. These …
Hybrid chance-constrained optimal power flow under load and renewable generation uncertainty using enhanced multi-fidelity graph neural networks
Power systems are transitioning toward renewable sources and electrification, introducing
significant uncertainties in generation and demand that optimal power flow (OPF) methods …
significant uncertainties in generation and demand that optimal power flow (OPF) methods …
Generative adversarial physics-informed neural networks for solving forward and inverse problem with small labeled samples
W Li, C Wang, H Guan, J Wang, J Yang… - … & Mathematics with …, 2025 - Elsevier
Physics-informed neural networks (PINNs) provide a deep learning framework for
numerically solving partial differential equations (PDEs), but there still remain some …
numerically solving partial differential equations (PDEs), but there still remain some …
Hybrid Physics-Infused 1 Dimensional-Convolutional Neural Network (1D-CNN) based Ensemble Learning Framework for Diesel Engine Fault Diagnostics
SK Singh, RP Khawale… - … of Computing and …, 2025 - asmedigitalcollection.asme.org
Due to their high thermal efficiency and long functional life, diesel engines have become
ubiquitous in automobiles. Diesel engines are vulnerable to component failure and sensor …
ubiquitous in automobiles. Diesel engines are vulnerable to component failure and sensor …
Hybrid Physics-Infused Machine Learning Framework For Fault Diagnostics and Prognostics in Cyber-Physical System Of Diesel Engine
SK Singh - 2024 - search.proquest.com
Fault diagnosis is required to ensure the safe operation of various equipment and enables
real-time monitoring of associated components. As a result, the demand for new cognitive …
real-time monitoring of associated components. As a result, the demand for new cognitive …
Enhancing Early Design Decisions through Lightweight, Multi-Fidelity Simulation with AutomataScales
P Chaisiriroj - 2024 - ir.library.oregonstate.edu
Early design decisions are critical in engineering as they significantly influence the overall
success, efficiency and, ultimately, cost of system development. Current simulation methods …
success, efficiency and, ultimately, cost of system development. Current simulation methods …