Physics-informed machine learning for data anomaly detection, classification, localization, and mitigation: A review, challenges, and path forward
MJ Zideh, P Chatterjee, AK Srivastava - IEEE Access, 2023 - ieeexplore.ieee.org
Advancements in digital automation for smart grids have led to the installation of
measurement devices like phasor measurement units (PMUs), micro-PMUs (-PMUs), and …
measurement devices like phasor measurement units (PMUs), micro-PMUs (-PMUs), and …
Generative ai and process systems engineering: The next frontier
This review article explores how emerging generative artificial intelligence (GenAI) models,
such as large language models (LLMs), can enhance solution methodologies within process …
such as large language models (LLMs), can enhance solution methodologies within process …
[HTML][HTML] Physically consistent neural networks for building thermal modeling: theory and analysis
Due to their high energy intensity, buildings play a major role in the current worldwide
energy transition. Building models are ubiquitous since they are needed at each stage of the …
energy transition. Building models are ubiquitous since they are needed at each stage of the …
[HTML][HTML] Formulating data-driven surrogate models for process optimization
Recent developments in data science and machine learning have inspired a new wave of
research into data-driven modeling for mathematical optimization of process applications …
research into data-driven modeling for mathematical optimization of process applications …
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 …
Knowledge-augmented deep learning and its applications: A survey
Deep learning models, though having achieved great success in many different fields over
the past years, are usually data-hungry, fail to perform well on unseen samples, and lack …
the past years, are usually data-hungry, fail to perform well on unseen samples, and lack …
Neural network design for impedance modeling of power electronic systems based on latent features
Data-driven approaches are promising to address the modeling issues of modern power
electronics-based power systems, due to the black-box feature. Frequency-domain analysis …
electronics-based power systems, due to the black-box feature. Frequency-domain analysis …
A novel domain knowledge-informed machine learning approach for modeling solid waste management systems
Sustainability challenges, such as solid waste management, are usually scientifically
complex and data scarce, which makes them not amenable to science-based analytical …
complex and data scarce, which makes them not amenable to science-based analytical …
Winert: Towards neural ray tracing for wireless channel modelling and differentiable simulations
In this paper, we work towards a neural surrogate to model wireless electro-magnetic
propagation effects in indoor environments. Such neural surrogates provide a fast …
propagation effects in indoor environments. Such neural surrogates provide a fast …
Learning robust state observers using neural odes
Relying on recent research results on Neural ODEs, this paper presents a methodology for
the design of state observers for nonlinear systems based on Neural ODEs, learning …
the design of state observers for nonlinear systems based on Neural ODEs, learning …