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Found 21 papers, 8 papers with code
From concrete mixture to structural design -- a holistic optimization procedure in the presence of uncertainties
Designing civil structures such as bridges, dams or buildings is a complex task requiring many synergies from several experts.
Multi-fidelity Constrained Optimization for Stochastic Black Box Simulators
Constrained optimization of the parameters of a simulator plays a crucial role in a design process.
A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty
A fully Bayesian formulation is proposed, combined with a sparsity-inducing prior in order to identify regions in the problem domain where the parametric closure is insufficient and where stochastic corrections to the Reynolds stress tensor are needed.
Interpretable reduced-order modeling with time-scale separation
To this end, we combine a non-linear autoencoder architecture with a time-continuous model for the latent dynamics in the complex space.
Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems
Neural Operators offer a powerful, data-driven tool for solving parametric PDEs as they can represent maps between infinite-dimensional function spaces.
Physics-enhanced Neural Networks in the Small Data Regime
Identifying the dynamics of physical systems requires a machine learning model that can assimilate observational data, but also incorporate the laws of physics.
Self-supervised optimization of random material microstructures in the small-data regime
While the forward and backward modeling of the process-structure-property chain has received a lot of attention from the materials community, fewer efforts have taken into consideration uncertainties.
Physics-aware, deep probabilistic modeling of multiscale dynamics in the Small Data regime
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
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.
A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables
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
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
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.
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 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
Uncertainty Quantification
+1
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.
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.
A data-driven model order reduction approach for Stokes flow through random porous media
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
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.
Bayesian model and dimension reduction for uncertainty propagation: applications in random media
Both components are represented with latent variables in a probabilistic graphical model and are simultaneously trained using Stochastic Variational Inference methods.
Probabilistic Reduced-Order Modeling for Stochastic Partial Differential Equations
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
We propose a data-driven, coarse-graining formulation in the context of equilibrium statistical mechanics.
Variational Bayesian strategies for high-dimensional, stochastic design problems
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.
Uncertainty Quantification
Vocal Bursts Intensity Prediction
