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09/10/2023
Physics-Aware Reduced-Order Modeling of Nonautonomous Advection-Dominated Problems
We present a variant of dynamic mode decomposition (DMD) for constructin...
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08/23/2023
Neural oscillators for magnetic hysteresis modeling
Hysteresis is a ubiquitous phenomenon in science and engineering; its mo...
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08/17/2023
Neural oscillators for generalization of physics-informed machine learning
A primary challenge of physics-informed machine learning (PIML) is its g...
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06/27/2023
Learning Nonautonomous Systems via Dynamic Mode Decomposition
We present a data-driven learning approach for unknown nonautonomous dyn...
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02/10/2023
Discovering Sparse Hysteresis Models for Piezoelectric Materials: A Data-Driven Study and Perspectives into Modelling Magnetic Hysteresis
This article presents an approach for modelling hysteresis in piezoelect...
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04/20/2022
Model Reduction via Dynamic Mode Decomposition
This work proposes a new framework of model reduction for parametric com...
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10/24/2021
Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties
Identifying the heterogeneous conductivity field and reconstructing the ...
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04/29/2021
Transfer Learning on Multi-Fidelity Data
Neural networks (NNs) are often used as surrogates or emulators of parti...
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03/01/2021
Information-geometry of physics-informed statistical manifolds and its use in data assimilation
The data-aware method of distributions (DA-MD) is a low-dimension data a...
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11/22/2020
Autonomous learning of nonlocal stochastic neuron dynamics
Neuronal dynamics is driven by externally imposed or internally generate...
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09/07/2020
Mutual Information for Explainable Deep Learning of Multiscale Systems
Timely completion of design cycles for multiscale and multiphysics syste...
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06/26/2020
GINNs: Graph-Informed Neural Networks for Multiscale Physics
We introduce the concept of a Graph-Informed Neural Network (GINN), a hy...
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05/07/2020
Learning on dynamic statistical manifolds
Hyperbolic balance laws with uncertain (random) parameters and inputs ar...
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04/13/2020
Extended Dynamic Mode Decomposition for Inhomogeneous Problems
Dynamic mode decomposition (DMD) is a powerful data-driven technique for...
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03/26/2020
Dynamic Mode Decomposition for Construction of Reduced-Order Models of Hyperbolic Problems with Shocks
Construction of reduced-order models (ROMs) for hyperbolic conservation ...
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03/01/2020
Markov Chain Monte Carlo with Neural Network Surrogates: Application to Contaminant Source Identification
Subsurface remediation often involves reconstruction of contaminant rele...
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01/30/2020
Data-Driven Discovery of Coarse-Grained Equations
A general method for learning probability density function (PDF) equatio...
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08/10/2019
Lagrangian Dynamic Mode Decomposition for Construction of Reduced-Order Models of Advection-Dominated Phenomena
Proper orthogonal decomposition (POD) and dynamic mode decomposition (DM...
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06/01/2019
Estimation of distributions via multilevel Monte Carlo with stratified sampling
We design and implement a novel algorithm for computing a multilevel Mon...
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01/06/2019