Search Results for author: Phaedon-Stelios Koutsourelakis

Found 14 papers, 5 papers with code

Physics-aware, deep probabilistic modeling of multiscale dynamics in the Small Data regime

no code implementations8 Feb 2021 Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis

The data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the context of multiscale problems.

Physics-aware, probabilistic model order reduction with guaranteed stability

no code implementations ICLR 2021 Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis

Given (small amounts of) time-series' data from a high-dimensional, fine-grained, multiscale dynamical system, we propose a generative framework for learning an effective, lower-dimensional, coarse-grained dynamical model that is predictive of the fine-grained system's long-term evolution but also of its behavior under different initial conditions.

Dimensionality Reduction Time Series

A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables

1 code implementation2 Jun 2020 Maximilian Rixner, Phaedon-Stelios Koutsourelakis

We advocate a probabilistic (Bayesian) model in which equalities that are available from the physics (e. g. residuals, conservation laws) can be introduced as virtual observables and can provide additional information through the likelihood.

Embedded-physics machine learning for coarse-graining and collective variable discovery without data

no code implementations24 Feb 2020 Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis

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.

Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems

1 code implementation30 Dec 2019 Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis

Data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the context of multiscale problems.

Small Data Image Classification

A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime

1 code implementation11 Feb 2019 Constantin Grigo, Phaedon-Stelios Koutsourelakis

The automated construction of coarse-grained models represents a pivotal component in computer simulation of physical systems and is a key enabler in various analysis and design tasks related to uncertainty quantification.

Small Data Image Classification Variational Inference

Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data

1 code implementation18 Jan 2019 Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, Paris Perdikaris

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.

Small Data Image Classification

Predictive Collective Variable Discovery with Deep Bayesian Models

1 code implementation18 Sep 2018 Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis

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.

Bayesian Inference Variational Inference

A data-driven model order reduction approach for Stokes flow through random porous media

no code implementations21 Jun 2018 Constantin Grigo, Phaedon-Stelios Koutsourelakis

Direct numerical simulation of Stokes flow through an impermeable, rigid body matrix by finite elements requires meshes fine enough to resolve the pore-size scale and is thus a computationally expensive task.

Beyond black-boxes in Bayesian inverse problems and model validation: applications in solid mechanics of elastography

no code implementations2 Mar 2018 Lukas Bruder, Phaedon-Stelios Koutsourelakis

This recasts the solution of both forward and inverse problems as probabilistic inference tasks where the problem's state variables should not only be compatible with the data but also with the governing equations as well.

Variational Inference

Bayesian model and dimension reduction for uncertainty propagation: applications in random media

no code implementations7 Nov 2017 Constantin Grigo, Phaedon-Stelios Koutsourelakis

Both components are represented with latent variables in a probabilistic graphical model and are simultaneously trained using Stochastic Variational Inference methods.

Dimensionality Reduction Variational Inference

Probabilistic Reduced-Order Modeling for Stochastic Partial Differential Equations

no code implementations6 Mar 2017 Constantin Grigo, Phaedon-Stelios Koutsourelakis

We discuss a Bayesian formulation to coarse-graining (CG) of PDEs where the coefficients (e. g. material parameters) exhibit random, fine scale variability.

Predictive Coarse-Graining

no code implementations26 May 2016 Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis

We propose a data-driven, coarse-graining formulation in the context of equilibrium statistical mechanics.

Model Selection

Variational Bayesian strategies for high-dimensional, stochastic design problems

no code implementations24 Jul 2015 Phaedon-Stelios Koutsourelakis

The solution of such problems is hindered not only by the usual difficulties encountered in UQ tasks (e. g. the high computational cost of each forward simulation, the large number of random variables) but also by the need to solve a nonlinear optimization problem involving large numbers of design variables and potentially constraints.

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