Learning regime‐dependent governing equations: A symbolic decision tree approach
Abstract Many chemical engineering systems are governed by mechanisms that switch across operating regimes, making the data‐driven discovery of regime‐dependent governing equations essential for predictive modeling, optimization, and control. We propose symbolic decision trees for the data‐driven discovery of regime‐dependent governing equations.
Ilias Mitrai +2 more
wiley +1 more source
SPA-QNAS: Improving Search Efficiency and Stability in Evolutionary Quantum Neural Architecture Search. [PDF]
Shang L, Cao H, Wu Y, Niu X, Chen J.
europepmc +1 more source
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin +4 more
wiley +1 more source
SALC-Net: A lightweight contour-preserving segmentation network for yak body segmentation in complex grazing environments. [PDF]
Zhang W +5 more
europepmc +1 more source
A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows +7 more
wiley +1 more source
A Gait-Conditioned Human-in-the-Loop Optimization Framework Based on Gait-Assistance-Response Relationship Model for Exoskeleton Assistance. [PDF]
Han J +7 more
europepmc +1 more source
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley +1 more source
Co-exposure to planned power outages and wildfire PM2.5 and acute healthcare utilization in California: A time-stratified case-crossover study. [PDF]
Wilner LB +8 more
europepmc +1 more source
Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser +6 more
wiley +1 more source
ReSNNance: an HDL generation framework for spiking neural networks. [PDF]
López-Asunción S +3 more
europepmc +1 more source

