activity
20242026
most citedTowards a Hybrid Digital Twin: Physics-Informed Neural Networks as Surrogate Model of a Reinforced Concrete Beam

14 citations · 16 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CE2026

Physics-Informed Sensitivity Analysis for Enhanced Structural Health Assessment: Test-Case for a Mixed Steel-Concrete Bridge

Jacopo Bonari, Francesca Marsili, Max von Danwitz +1

Bridges are vital components of transportation systems, that serve as essential links ensuring the safe and efficient movement of people, goods, and emergency responders, especiall…

math.NA2026

Rapid Identification of Moving Contaminant Sources Through Physics-Based Modelling

Marco Mattuschka, Jacopo Bonari, Max von Danwitz +1

In an act of sabotage or terrorism, hazardous material might be released deliberately into the atmosphere to threaten individuals, e.g., those operating critical infrastructure. Ha…

math.NA2025

Sparse Source Identification in Transient Advection-Diffusion Problems with a Primal-Dual-Active-Point Strategy

Marco Mattuschka, Daniel Walter, Max von Danwitz +1

This work presents a mathematical model to enable rapid prediction of airborne contaminant transport based on scarce sensor measurements. The method is designed for applications in…

math.NA2025

Goal-oriented optimal sensor placement for PDE-constrained inverse problems in crisis management

Marco Mattuschka, Noah An der Lan, Max von Danwitz +2

This paper presents a novel framework for goal-oriented optimal static sensor placement and dynamic sensor steering in PDE-constrained inverse problems, utilizing a Bayesian approa…

cs.CE2024★ 2 cited

Contaminant Dispersion Simulation in a Digital Twin Framework for Critical Infrastructure Protection

Max von Danwitz, Jacopo Bonari, Philip Franz +3

A digital twin framework for rapid predictions of atmospheric contaminant dispersion is developed to support informed decision making in emergency situations. In an offline prepara…

cs.CE2024★ 14 cited

Towards a Hybrid Digital Twin: Physics-Informed Neural Networks as Surrogate Model of a Reinforced Concrete Beam

Tarik Sahin, Daniel Wolff, Max von Danwitz +1

In this study, we investigate the potential of fast-to-evaluate surrogate modeling techniques for developing a hybrid digital twin of a steel-reinforced concrete beam, serving as a…