Learning without Data: Physics-Informed Neural Networks for Fast Time-Domain Simulation

Stiasny, Jochen; Chevalier, Samuel; Chatzivasileiadis, Spyros

doi:10.1109/smartgridcomm51999.2021.9631995

Cited by 12 publications

(2 citation statements)

References 19 publications

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“…The proposed framework can be applied to a broad range of physical phenomena to estimate the governing parameters and identify the mathematical expressions of the missing parts of a partial knowledge of the physics. Both PINNs and X-TFC are effective and generalizable for solving problems and dynamical systems involving both ODEs [29,44,[49][50][51][52][53][54][55][56][57] and PDEs [31,[58][59][60][61][62][63], in fields such as rarefied-gas dynamics, optimal control, epidemiology, radiative transfer, chemical kinetics, and many others. X-TFC, in Ref.…”

Section: Plos Computational Biologymentioning

confidence: 99%

AI-Aristotle: A physics-informed framework for systems biology gray-box identification

Ahmadi Daryakenari,

De Florio,

Shukla

et al. 2024

PLoS Comput Biol

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Discovering mathematical equations that govern physical and biological systems from observed data is a fundamental challenge in scientific research. We present a new physics-informed framework for parameter estimation and missing physics identification (gray-box) in the field of Systems Biology. The proposed framework—named AI-Aristotle—combines the eXtreme Theory of Functional Connections (X-TFC) domain-decomposition and Physics-Informed Neural Networks (PINNs) with symbolic regression (SR) techniques for parameter discovery and gray-box identification. We test the accuracy, speed, flexibility, and robustness of AI-Aristotle based on two benchmark problems in Systems Biology: a pharmacokinetics drug absorption model and an ultradian endocrine model for glucose-insulin interactions. We compare the two machine learning methods (X-TFC and PINNs), and moreover, we employ two different symbolic regression techniques to cross-verify our results. To test the performance of AI-Aristotle, we use sparse synthetic data perturbed by uniformly distributed noise. More broadly, our work provides insights into the accuracy, cost, scalability, and robustness of integrating neural networks with symbolic regressors, offering a comprehensive guide for researchers tackling gray-box identification challenges in complex dynamical systems in biomedicine and beyond.

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Section: Plos Computational Biologymentioning

confidence: 99%

AI-Aristotle: A physics-informed framework for systems biology gray-box identification

Ahmadi Daryakenari,

De Florio,

Shukla

et al. 2024

PLoS Comput Biol

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“…PINNs were first applied to power systems in [34] to predict swing equation dynamics. They have since additionally been used for system identification [35], transient stability predictions [36], and for learning grid dynamics without simulation data [37]. Beyond ODE simulation and trajectory prediction, physics and sensitivity informed methods have also been utilized for regularizing models related to power distribution grid optimization [38], ACOPF [39], [40], DCOPF [41], parameter estimation [42], and risk-aware voltage optimization via "riskregularization" [43].…”

Section: Model Training and Physics-based Regularizationmentioning

confidence: 99%

Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems

Stiasny¹,

Chevalier²,

Nellikkath³

et al. 2022

Preprint

Self Cite

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Deep decarbonization of the energy sector will require massive penetration of stochastic renewable energy resources and an enormous amount of grid asset coordination; this represents a challenging paradigm for the power system operators who are tasked with maintaining grid stability and security in the face of such changes. With its ability to learn from complex datasets and provide predictive solutions on fast timescales, machine learning (ML) is well-posed to help overcome these challenges as power systems transform in the coming decades. In this work, we outline five key challenges (dataset generation, data pre-processing, model training, model assessment, and model embedding) associated with building trustworthy ML models which learn from physics-based simulation data. We then demonstrate how linking together individual modules, each of which overcomes a respective challenge, at sequential stages in the machine learning pipeline can help enhance the overall performance of the training process. In particular, we implement methods that connect different elements of the learning pipeline through feedback, thus "closing the loop" between model training, performance assessments, and re-training. We demonstrate the effectiveness of this framework, its constituent modules, and its feedback connections by learning the N-1 small-signal stability margin associated with a detailed model of a proposed North Sea Wind Power Hub system.

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Computational Intelligence-Based Learning Techniques to Handle Uncertainty and Large Data Sets

Ramasubramanian,

Zhang,

Kelly

et al. 2024

CIGRE Green Books

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Learning without Data: Physics-Informed Neural Networks for Fast Time-Domain Simulation

Cited by 12 publications

References 19 publications

AI-Aristotle: A physics-informed framework for systems biology gray-box identification

AI-Aristotle: A physics-informed framework for systems biology gray-box identification

Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems

Computational Intelligence-Based Learning Techniques to Handle Uncertainty and Large Data Sets

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