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20082026
most citedPhysics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data

1.1k citations · 1.4k across the 43 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

math.OC2023

From concrete mixture to structural design -- a holistic optimization procedure in the presence of uncertainties

Atul Agrawal, Erik Tamsen, Phaedon-Stelios Koutsourelakis +1

Designing civil structures such as bridges, dams or buildings is a complex task requiring many synergies from several experts. Each is responsible for different parts of the proces…

cs.CE2023★ 3 cited

Model bias identification for Bayesian calibration of stochastic digital twins of bridges

Daniel Andrés Arcones, Martin Weiser, Phaedon-Stelios Koutsourelakis +1

Simulation-based digital twins must provide accurate, robust and reliable digital representations of their physical counterparts. Quantifying the uncertainty in their predictions p…

math.OC2023★ 1 cited

Multi-fidelity Constrained Optimization for Stochastic Black Box Simulators

Atul Agrawal, Kislaya Ravi, Phaedon-Stelios Koutsourelakis +1

Constrained optimization of the parameters of a simulator plays a crucial role in a design process. These problems become challenging when the simulator is stochastic, computationa…

physics.flu-dyn2023★ 1 cited

Physics-Informed Tensor Basis Neural Network for Turbulence Closure Modeling

Leon Riccius, Atul Agrawal, Phaedon-Stelios Koutsourelakis

Despite the increasing availability of high-performance computational resources, Reynolds-Averaged Navier-Stokes (RANS) simulations remain the workhorse for the analysis of turbule…

physics.flu-dyn2023

A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty

Atul Agrawal, Phaedon-Stelios Koutsourelakis

We propose a data-driven, closure model for Reynolds-averaged Navier-Stokes (RANS) simulations that incorporates aleatoric, model uncertainty. The proposed closure consists of two…

stat.ML2023★ 1 cited

Interpretable reduced-order modeling with time-scale separation

Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis, Petros Koumoutsakos

Partial Differential Equations (PDEs) with high dimensionality are commonly encountered in computational physics and engineering. However, finding solutions for these PDEs can be c…