Search Results for author:
Found 21 papers, 15 papers with code
Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties
We use a convolutional adversarial autoencoder (CAAE) for the parameterization of the heterogeneous non-Gaussian conductivity field with a low-dimensional latent representation.
Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning
One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade.
A Bayesian Multiscale Deep Learning Framework for Flows in Random Media
In addition, it is challenging to develop accurate surrogate and uncertainty quantification models for high-dimensional problems governed by stochastic multiscale PDEs using limited training data.
Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems
In this way, the global features are identified in the coarse-scale with inference of low-dimensional variables and inexpensive forward computation, and the local features are refined and corrected in the fine-scale.
Transformers for Modeling Physical Systems
Transformers are widely used in natural language processing due to their ability to model longer-term dependencies in text.
Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder
In this work, we assess the predictive capabilities of a molecular generative model developed based on variational inference and graph theory in the small data regime.
Solving inverse problems using conditional invertible neural networks
In this work, we construct a two- and three-dimensional inverse surrogate models consisting of an invertible and a conditional neural network trained in an end-to-end fashion with limited training data.
Multi-fidelity Generative Deep Learning Turbulent Flows
The resulting surrogate is able to generate physically accurate turbulent realizations at a computational cost magnitudes lower than that of a high-fidelity simulation.
Embedded-physics machine learning for coarse-graining and collective variable discovery without data
Rather than separating model learning from the data-generation procedure - the latter relies on simulating atomistic motions governed by force fields - we query the atomistic force field at sample configurations proposed by the predictive coarse-grained model.
Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non-Gaussian hydraulic conductivities
In addition, a deep residual dense convolutional network (DRDCN) is proposed for surrogate modeling of forward models with high-dimensional and highly-complex mappings.
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 surrogate modeling and uncertainty quantification for a vast number of physical systems.
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data
Surrogate modeling and uncertainty quantification tasks for PDE systems are most often considered as supervised learning problems where input and output data pairs are used for training.
Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification
Results indicate that, with relatively limited training data, the deep autoregressive neural network consisting of 27 convolutional layers is capable of providing an accurate approximation for the high-dimensional model input-output relationship.
Predictive Collective Variable Discovery with Deep Bayesian Models
In this work, we formulate the discovery of CVs as a Bayesian inference problem and consider the CVs as hidden generators of the full-atomistic trajectory.
Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion
A structured Bayesian Gaussian process latent variable model is used both to construct a low-dimensional generative model of the sample-based stochastic prior as well as a surrogate for the forward evaluation.
Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks
Uncertainty quantification for such data-driven models is essential since their predictive capability rapidly declines as they are tested for flow physics that deviate from that in the training data.
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
A training strategy combining a regression loss and a segmentation loss is proposed in order to better approximate the discontinuous saturation field.
Structured Bayesian Gaussian process latent variable model
We introduce a Bayesian Gaussian process latent variable model that explicitly captures spatial correlations in data using a parameterized spatial kernel and leveraging structure-exploiting algebra on the model covariance matrices for computational tractability.
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification
We are interested in the development of surrogate models for uncertainty quantification and propagation in problems governed by stochastic PDEs using a deep convolutional encoder-decoder network in a similar fashion to approaches considered in deep learning for image-to-image regression tasks.
Predictive Coarse-Graining
We propose a data-driven, coarse-graining formulation in the context of equilibrium statistical mechanics.
Variational Reformulation of Bayesian Inverse Problems
The classical approach to inverse problems is based on the optimization of a misfit function.
