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07/05/2023
A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty
We propose a data-driven, closure model for Reynolds-averaged Navier-Sto...
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03/03/2023
Interpretable reduced-order modeling with time-scale separation
Partial Differential Equations (PDEs) with high dimensionality are commo...
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09/06/2022
Semi-supervised Invertible DeepONets for Bayesian Inverse Problems
Deep Operator Networks (DeepONets) offer a powerful, data-driven tool fo...
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11/19/2021
Physics-enhanced Neural Networks in the Small Data Regime
Identifying the dynamics of physical systems requires a machine learning...
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08/05/2021
Self-supervised optimization of random material microstructures in the small-data regime
While the forward and backward modeling of the process-structure-propert...
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02/08/2021
Physics-aware, deep probabilistic modeling of multiscale dynamics in the Small Data regime
The data-based discovery of effective, coarse-grained (CG) models of hig...
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01/14/2021
Physics-aware, probabilistic model order reduction with guaranteed stability
Given (small amounts of) time-series' data from a high-dimensional, fine...
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06/02/2020
A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables
The data-centric construction of inexpensive surrogates for fine-grained...
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01/09/2020
A Generalized Probabilistic Learning Approach for Multi-Fidelity Uncertainty Propagation in Complex Physical Simulations
Two of the most significant challenges in uncertainty propagation pertai...
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12/30/2019
Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems
Data-based discovery of effective, coarse-grained (CG) models of high-di...
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02/11/2019
A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime
The automated construction of coarse-grained models represents a pivotal...
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01/18/2019
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data
Surrogate modeling and uncertainty quantification tasks for PDE systems ...
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09/18/2018
Predictive Collective Variable Discovery with Deep Bayesian Models
Extending spatio-temporal scale limitations of models for complex atomis...
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06/21/2018
A data-driven model order reduction approach for Stokes flow through random porous media
Direct numerical simulation of Stokes flow through an impermeable, rigid...
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03/02/2018
Beyond black-boxes in Bayesian inverse problems and model validation: applications in solid mechanics of elastography
The present paper is motivated by one of the most fundamental challenges...
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11/07/2017
Bayesian model and dimension reduction for uncertainty propagation: applications in random media
Well-established methods for the solution of stochastic partial differen...
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03/06/2017
Probabilistic Reduced-Order Modeling for Stochastic Partial Differential Equations
We discuss a Bayesian formulation to coarse-graining (CG) of PDEs where ...
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05/26/2016