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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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02/04/2021
Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems
Estimation of spatially-varying parameters for computationally expensive...
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09/29/2020
Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder
Recent advances in artificial intelligence have propelled the developmen...
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07/31/2020
Solving inverse problems using conditional invertible neural networks
Inverse modeling for computing a high-dimensional spatially-varying prop...
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06/08/2020
Multi-fidelity Generative Deep Learning Turbulent Flows
In computational fluid dynamics, there is an inevitable trade off betwee...
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06/26/2019
Integration of adversarial autoencoders with residual dense convolutional networks for inversion of solute transport in non-Gaussian conductivity fields
Characterization of a non-Gaussian channelized conductivity field in sub...
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06/13/2019
Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks
In recent years, deep learning has proven to be a viable methodology for...
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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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12/22/2018
Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification
Identification of a groundwater contaminant source simultaneously with t...
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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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07/11/2018
Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion
We introduce a methodology for nonlinear inverse problems using a variat...
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07/08/2018
Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks
Data-driven methods for improving turbulence modeling in Reynolds-Averag...
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07/02/2018
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
Surrogate strategies are used widely for uncertainty quantification of g...
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05/22/2018
Structured Bayesian Gaussian process latent variable model
We introduce a Bayesian Gaussian process latent variable model that expl...
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01/21/2018
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification
We are interested in the development of surrogate models for uncertainty...
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05/26/2016
Predictive Coarse-Graining
We propose a data-driven, coarse-graining formulation in the context of ...
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10/21/2014