activity
20202026
most citedCertified machine learning: A posteriori error estimation for physics-informed neural networks

11 citations · 20 across the 7 of their papers we have counts for

collaborators

7 papers

math.OC2026

L2-L2-gain bounds for quadratic output systems

Birgit Hillebrecht

We derive an explicit bound for the L2-L2-gain of linear time-invariant systems whose output is a quadratic function of the state and the input. Such systems appear naturally in ma…

math.OC2026

Estimation of the input-to-state stability gain functions from finite-dimensional approximations

Birgit Hillebrecht, Benjamin Unger

Since the concept of input-to-state stability (ISS) was introduced, it has been extensively investigated for finite-dimensional control systems and has recently received attention…

math.NA2025

Prediction error certification for PINNs: Theory, computation, and application to Stokes flow

Birgit Hillebrecht, Benjamin Unger

Rigorous error estimation is a fundamental topic in numerical analysis. With the increasing use of physics-informed neural networks (PINNs) for solving partial differential equatio…

math.OC2025

model order reduction for quadratic output systems

Birgit Hillebrecht, Benjamin Unger

Linear time-invariant quadratic output (LTIQO) systems generalize linear time-invariant systems to nonlinear regimes. Problems of this class occur in multiple applications naturall…

cs.LG2022★ 6 cited

Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs

Birgit Hillebrecht, Benjamin Unger

Prediction error quantification in machine learning has been left out of most methodological investigations of neural networks, for both purely data-driven and physics-informed app…

cs.LG2022★ 11 cited

Certified machine learning: A posteriori error estimation for physics-informed neural networks

Birgit Hillebrecht, Benjamin Unger

Physics-informed neural networks (PINNs) are one popular approach to incorporate a priori knowledge about physical systems into the learning framework. PINNs are known to be robust…

cond-mat.str-el2020★ 3 cited

Continuum limit of lattice quasielectron wavefunctions

Aniket Patra, Birgit Hillebrecht, Anne E. B. Nielsen

Trial states describing anyonic quasiholes in the Laughlin state were found early on, and it is therefore natural to expect that one should also be able to create anyonic quasielec…