Physics-informed machine learning
PulseAugur coverage of Physics-informed machine learning — every cluster mentioning Physics-informed machine learning across labs, papers, and developer communities, ranked by signal.
-
Physics-Informed ML boosts PHM performance, review finds · 1 source tracked
A systematic literature review of 212 studies reveals that Physics-Informed Machine Learning (PIML) is increasingly applied to Prognostics and Health Management (PHM) tasks. While PIML models demonstrate improved predic…
-
New PIKS method offers universal physics-informed kernel learning
Researchers have introduced Physics-Informed Kernel methodS (PIKS), a novel approach to physics-informed machine learning that aims to overcome the limitations of existing methods. Unlike physics-informed neural network…
-
New taxonomy proposed for evaluating discovered scientific laws
A new paper published on arXiv addresses the complex challenge of evaluating discovered Partial Differential Equations (PDEs). The research proposes the first taxonomy of PDE evaluation metrics, highlighting the need to…
-
Research paper on PIML failures in traffic flow modeling withdrawn
A research paper by Yuan-Zheng Lei, initially submitted to arXiv in May 2025 and later withdrawn, explored the theoretical and experimental reasons behind the failures of physics-informed machine learning (PIML) in macr…
-
Physics-guided AI enables safer robotic radiation source localization
Researchers have developed a new framework for robotic radiation source localization (RSL) that utilizes a physics-informed machine learning (PIML) model. This approach allows robots to accurately estimate radiation sou…
-
New benchmark evaluates physics-informed ML for material design decisions
Researchers have introduced pinn-gym, a new benchmark designed to evaluate physics-informed machine learning (PIML) models in material design. Traditional evaluation methods focusing on curve error are insufficient for …
-
Physics-informed AI improves flood prediction in scarce data
Researchers have developed a new Physics-Informed Machine Learning (PIML) framework to improve short-term flood forecasting. This approach integrates hydrological knowledge directly into the loss function of an LSTM mod…
-
PIML framework enhances lunar rover thermal modeling accuracy and speed
Researchers have developed a novel Physics-Informed Machine Learning (PIML) framework to improve the thermal modeling of lunar rovers. This approach integrates a transfer neural network (TNN) that adaptively determines …
-
New research probes generalization limits of physics-informed AI models
Two new research papers explore the generalization capabilities of physics-informed machine learning models. The first paper introduces a comprehensive benchmark to evaluate physics foundation models across various phys…