Results 111 to 120 of about 11,198,121 (288)

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

, 2023
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains.
Paulson, Joel A., Drgoňa, Ján, Nagy, Zoltan, Schwan, Roland, Di Cairano, Stefano, Cortez, Wenceslao Shaw, Carron, Andrea, Jones, Colin, Nghiem, Truong X., Vrabie, Draguna L., Dey, Biswadip, Zeilinger, Melanie N., Chakrabarty, Ankush   +12 more
core  

Embedding Physics into Machine Learning: A Review of Physics Informed Neural Networks as Partial Differential Equation Forward Solvers

Tsinghua Science and Technology
Partial Differential Equations (PDEs) are a fundamental class of mathematical models widely used for modeling continuous systems across scientific and engineering disciplines.
Wenhui Fan, Xujia Chen
doaj   +1 more source

Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigm

CoRR
31 pages, 4 figures, 3 tables.
openaire   +3 more sources

Uncertainty-Aware Diagnostics for Physics-Informed Machine Learning

CoRR
Physics-informed machine learning (PIML) integrates prior physical information, often in the form of differential equation constraints, into the process of fitting machine learning models to physical data. Popular PIML approaches, including neural operators, physics-informed neural networks, neural ordinary differential equations, and neural discrete ...
Mara Daniels, Liam Hodgkinson, Michael W. Mahoney   +2 more
openaire   +2 more sources

Fiber‐Based Wearable Electronics as Adaptive Biointerfaces for Healthcare Applications

Advanced Functional Materials, EarlyView.
Fiber‐based wearable electronics have been extensively studied as adaptive healthcare biointerfaces. In this review, we classify and introduce fiber‐based systems according to biointerface design, conductive materials, transduction mechanisms, and healthcare applications, and provide a perspective on their translation toward reliable, continuous ...
Sangwoo Hong, Jinsol Kim, Yoonjae Jeong, Sejong Kang, Daeho Lee, Seung Hwan Ko   +5 more
wiley   +1 more source

A physically-informed machine learning model for freeform bending

Journal of Intelligent Manufacturing
Abstract This work aims at a fast computational process model of the free-form bending process. It proposes a novel physically-informed machine learning model, which is trained with experimental data of bending constant radii and utilizes additional physical bending knowledge by integrating Timoshenko’s beam theory.
Philipp Lechner, Lorenzo Scandola, Daniel Maier, Christoph Hartmann 0003, Yevgen Rizaiev, Mona Lieb   +5 more
openaire   +3 more sources

Mechanics of Telson Ridges in Mantis Shrimp for Impact Analysis and Implications for Bioinspired Elastic Shell Designs

Advanced Functional Materials, EarlyView.
The mantis shrimp telson reveals how ridged biological shields control deformation under impact. Inspired by this architecture, telson‐inspired ridged curved shells transform ridge geometry and macroscopic curvature into bidirectional snap‐through, tunable stiffness, and enhanced hysteretic energy dissipation for multifunctional architected materials ...
Phani Saketh Dasika, Adwait Trikanad, Wei Huang, Taige Hao, David Kisailus, Pablo D. Zavattieri   +5 more
wiley   +1 more source

Systematic Review on Physics-Informed Machine Learning for Fatigue and Fracture Analysis and Prediction

This bibliographic review systematically surveys advancements in applying physics-informed machine learning to fatigue and fracture analysis across diverse materials and ...
Larissa Gomes Simão, Eduardo Atem de Carvalho, Antônio José da Silva Neto, Rogerio Atem de Carvalho   +3 more
core   +1 more source

Modeling global surface dust deposition using physics-informed neural networks

Communications Earth & Environment
Paleoclimatic measurements serve to understand Earth System processes and evaluate climate model performances. However, their spatial coverage is generally sparse and unevenly distributed across the globe.
Constanza A. Molina Catricheo, Fabrice Lambert, Julien Salomon, Elwin van ’t Wout   +3 more
doaj   +1 more source

Analog Classification of Human Hand Gestures Exploiting Dendritic Integration With an Organic Artificial Soma

Advanced Functional Materials, EarlyView.
An organic neuromorphic architecture for the classification of human motor behavior is presented and validated. It performs somatic integration by linearly combining the activity from three muscles. An investigation of synaptic weights is presented and discussed in relationship with the classification performance.
Ilenia Sergi, Federico Rondelli, Francesco Torricelli, Federica Velluto, Anna De Salvo, Michele Di Lauro, Luciano Fadiga, Fabio Biscarini   +7 more
wiley   +1 more source