Results 21 to 30 of about 11,198,121 (288)

Efficient machine learning of solute segregation energy based on physics-informed features

Scientific Reports, 2023
Machine learning models solute segregation energy based on appropriate features of segregation sites. Lumping many features together can give a decent accuracy but may suffer the curse of dimensionality.
Zongyi Ma, Zhiliang Pan
doaj   +1 more source

Physics‐Informed Machine Learning for Optical Modes in Composites

Advanced Photonics Research, 2022
Herein, example of study of optical modes propagating through spatially periodic composites is used to demonstrate that embedding physics‐driven constraints into machine‐learning process can dramatically improve accuracy and generalizability of resulting
Abantika Ghosh, Mohannad Elhamod, Jie Bu, Wei-Cheng Lee, Anuj Karpatne, Viktor A Podolskiy   +5 more
doaj   +1 more source

Model Predictive Control Technique for Ducted Fan Aerial Vehicles Using Physics-Informed Machine Learning

Drones, 2022
This paper proposes a model predictive control (MPC) approach for ducted fan aerial robots using physics-informed machine learning (ML), where the task is to fully exploit the capabilities of the predictive control design with an accurate dynamic model ...
Tayyab Manzoor, Hailong Pei, Zhongqi Sun, Zihuan Cheng   +3 more
doaj   +1 more source

Scalable algorithms for physics-informed neural and graph networks

Data-Centric Engineering, 2022
Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available.
Khemraj Shukla, Mengjia Xu, Nathaniel Trask, George E. Karniadakis   +3 more
doaj   +1 more source

Physics-informed Machine Learning of Parameterized Fundamental Diagrams

CoRR, 2022
Fundamental diagrams describe the relationship between speed, flow, and density for some roadway (or set of roadway) configuration(s). These diagrams typically do not reflect, however, information on how speed-flow relationships change as a function of exogenous variables such as curb configuration, weather or other exogenous, contextual information ...
James Koch, Thomas Maxner, Vinay Amatya, Andisheh Ranjbari, Chase Dowling   +4 more
openaire   +3 more sources

Solution of Schrödinger Equation for Quantum Systems via Physics-Informed Neural Networks [PDF]

, 2023
openThe numerous successes achieved by machine learning techniques in many technical areas have sparked interest in the scientific community for their application in science.
ZINESI, PAOLO
core  

Machine learning to optimize additive manufacturing for visible photonics

Nanophotonics, 2023
Additive manufacturing has become an important tool for fabricating advanced systems and devices for visible nanophotonics. However, the lack of simulation and optimization methods taking into account the essential physics of the optimization process ...
Lininger Andrew, Aththanayake Akeshi, Boyd Jonathan, Ali Omar, Goel Madhav, Jizhe Yangheng, Hinczewski Michael, Strangi Giuseppe   +7 more
doaj   +1 more source

Predicting glass structure by physics-informed machine learning

npj Computational Materials, 2022
Machine learning (ML) is emerging as a powerful tool to predict the properties of materials, including glasses. Informing ML models with knowledge of how glass composition affects short-range atomic structure has the potential to enhance the ability of ...
Mikkel L. Bødker, Mathieu Bauchy, Tao Du, John C. Mauro, Morten M. Smedskjaer   +4 more
doaj   +1 more source

A Physics-Informed Convolutional Neural Network with Custom Loss Functions for Porosity Prediction in Laser Metal Deposition

Sensors, 2022
Physics-informed machine learning is emerging through vast methodologies and in various applications. This paper discovers physics-based custom loss functions as an implementable solution to additive manufacturing (AM).
Erin McGowan, Vidita Gawade, Weihong (Grace) Guo   +2 more
doaj   +1 more source

Physics Informed Neural Networks in Temporal Graphs [PDF]

, 2023
openLo scopo della tesi è quello di sviluppare nuove tecniche di interpretable physics informed machine learning per trovare modelli epidemiologici, e comparare queste tecniche ad altre già esistenti.
VISENTIN, FILIPPO
core