A Tutorial on Physics Constrained Machine Learning for Hybrid Modeling, Optimization, and Control
Paper recorded by Signals 4 on 2026-10-23 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-23 on arXiv · recorded by Signals 4 on 2026-10-26
Category: cs.LG · 机器学习 · first seen 2026-10-26
Abstract
Physics-constrained machine learning (PCML) integrates domain knowledge, first-principles, and physical constraints into AI/ML models. In this work, we present a step-by-step tutorial of a modular PCML framework, KKT-HardNet, and illustrate how it could be used for hybrid modeling, process simulation, optimization and model predictive control in a single platform. KKT-HardNet combines fast inferen
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Cite this page: A Tutorial on Physics Constrained Machine Learning for Hybrid Modeling, Optimization, and Control: the #184 most recent of 749 cs.LG papers we have recorded (as of 2026-10-23). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/a-tutorial-on-physics-constrained-machine-learning-for-hybrid-modeling-optimizat.html
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