Driven by data or derived through physics? a review of hybrid physics guided machine learning techniques with cyber-physical system (cps) focus
A multitude of cyber-physical system (CPS) applications, including design, control,
diagnosis, prognostics, and a host of other problems, are predicated on the assumption of …
diagnosis, prognostics, and a host of other problems, are predicated on the assumption of …
Modeling of deformable objects for robotic manipulation: A tutorial and review
Manipulation of deformable objects has given rise to an important set of open problems in
the field of robotics. Application areas include robotic surgery, household robotics …
the field of robotics. Application areas include robotic surgery, household robotics …
Simulation intelligence: Towards a new generation of scientific methods
The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …
computing, where a motif is an algorithmic method that captures a pattern of computation …
Flexible neural representation for physics prediction
Flexible neural representation for physics prediction Page 1 Flexible Neural Representation for
Physics Prediction Damian Mrowca1,⇤, Chengxu Zhuang2,⇤, Elias Wang3,⇤, Nick Haber2,4,5 …
Physics Prediction Damian Mrowca1,⇤, Chengxu Zhuang2,⇤, Elias Wang3,⇤, Nick Haber2,4,5 …
Surrogate modelling for an aircraft dynamic landing loads simulation using an LSTM AutoEncoder-based dimensionality reduction approach
M Lazzara, M Chevalier, M Colombo, JG Garcia… - Aerospace Science and …, 2022 - Elsevier
Surrogate modelling can alleviate the computational burden of design activities as they rely
on multiple evaluations of high-fidelity models. However, the learning task can be adversely …
on multiple evaluations of high-fidelity models. However, the learning task can be adversely …
Deep molecular representation learning via fusing physical and chemical information
Molecular representation learning is the first yet vital step in combining deep learning and
molecular science. To push the boundaries of molecular representation learning, we present …
molecular science. To push the boundaries of molecular representation learning, we present …
Physics-infused fuzzy generative adversarial network for robust failure prognosis
Prognostics aid in the longevity of fielded systems or products. Quantifying the system's
current health enable prognosis to enhance the operator's decision-making to preserve the …
current health enable prognosis to enhance the operator's decision-making to preserve the …
[HTML][HTML] Cyber-physical systems in non-rigid assemblies: A methodology for the calibration of deformable object reconstruction models
N Theodoropoulos, E Kampourakis, D Andronas… - Journal of Manufacturing …, 2023 - Elsevier
Despite the advances in robot agent cognition and systems' decision-making, under the
prism of cyber-physical systems and industrial metaverse, the manufacturing processes …
prism of cyber-physical systems and industrial metaverse, the manufacturing processes …
When deep learning meets data alignment: A review on deep registration networks (drns)
This paper reviews recent deep learning-based registration methods. Registration is the
process that computes the transformation that aligns datasets, and the accuracy of the result …
process that computes the transformation that aligns datasets, and the accuracy of the result …
Forward prediction for physical reasoning
Physical reasoning requires forward prediction: the ability to forecast what will happen next
given some initial world state. We study the performance of state-of-the-art forward …
given some initial world state. We study the performance of state-of-the-art forward …