Reconstructing computational system dynamics from neural data with recurrent neural networks
Computational models in neuroscience usually take the form of systems of differential
equations. The behaviour of such systems is the subject of dynamical systems theory …
equations. The behaviour of such systems is the subject of dynamical systems theory …
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 …
A tutorial on solving ordinary differential equations using Python and hybrid physics-informed neural network
We present a tutorial on how to directly implement integration of ordinary differential
equations through recurrent neural networks using Python. In order to simplify the …
equations through recurrent neural networks using Python. In order to simplify the …
Physics-informed machine learning in prognostics and health management: State of the art and challenges
Prognostics and health management (PHM) plays a constructive role in the equipment's
entire life health service. It has long benefited from intensive research into physics modeling …
entire life health service. It has long benefited from intensive research into physics modeling …
Physics-integrated variational autoencoders for robust and interpretable generative modeling
N Takeishi, A Kalousis - Advances in Neural Information …, 2021 - proceedings.neurips.cc
Integrating physics models within machine learning models holds considerable promise
toward learning robust models with improved interpretability and abilities to extrapolate. In …
toward learning robust models with improved interpretability and abilities to extrapolate. In …
A survey of Bayesian calibration and physics-informed neural networks in scientific modeling
FAC Viana, AK Subramaniyan - Archives of Computational Methods in …, 2021 - Springer
Computer simulations are used to model of complex physical systems. Often, these models
represent the solutions (or at least approximations) to partial differential equations that are …
represent the solutions (or at least approximations) to partial differential equations that are …
Vehicle lateral dynamics-inspired hybrid model using neural network for parameter identification and error characterization
Z Zhou, Y Wang, G Zhou, X Liu, M Wu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Autonomous vehicle requires a high-precision lateral dynamics model for path following and
lateral stability control. However, existing physical models suffer from low accuracy due to …
lateral stability control. However, existing physical models suffer from low accuracy due to …
Hybrid physics-infused 1D-CNN based deep learning framework for diesel engine fault diagnostics
Fault diagnosis is required to ensure the safe operation of various equipment and enables
real-time monitoring of associated components. As a result, the demand for new cognitive …
real-time monitoring of associated components. As a result, the demand for new cognitive …
AI and expert insights for sustainable energy future
MSS Danish - Energies, 2023 - mdpi.com
This study presents an innovative framework for leveraging the potential of AI in energy
systems through a multidimensional approach. Despite the increasing importance of …
systems through a multidimensional approach. Despite the increasing importance of …
Recent advances in deep learning models: a systematic literature review
R Malhotra, P Singh - Multimedia Tools and Applications, 2023 - Springer
In recent years, deep learning has evolved as a rapidly growing and stimulating field of
machine learning and has redefined state-of-the-art performances in a variety of …
machine learning and has redefined state-of-the-art performances in a variety of …