most citedUnsupervised Material Fingerprinting: Ultra-fast hyperelastic model discovery from full-field experimental measurements

2 citations · 2 across the 9 of their papers we have counts for

collaborators

9 papers

cs.AI2026

Benchmarking data-driven material models on the classic Treloar dataset

Hagen Holthusen, Moritz Flaschel, Denisa Martonová +1

Machine learning is rapidly reshaping constitutive modeling, offers new ways to learn material behavior directly from experimental data, and challenges long-established modeling pa…

physics.comp-ph2026

Uncertainty quantification in mechanics: A unified Bayesian perspective

Sascha Ranftl, Malte Rolf, Gerhard A. Holzapfel +1

Uncertainty quantification (UQ) is essential to experimental mechanics, but has become particularly relevant in computational mechanics, manifesting in two fundamental problem type…

cs.CE2026

Neural operators solve inverse problems for constitutive model discovery

Moritz Flaschel, Burigede Liu, Ellen Kuhl

Characterizing the mechanical response of materials traditionally requires solving optimization problems in which model parameters are calibrated or trained to minimize the discrep…

cs.CE2026

Adaptive Material Fingerprinting for the fast discovery of polyconvex feature combinations in isotropic and anisotropic hyperelasticity

Moritz Flaschel, Hagen Holthusen, Denisa Martonová +1

We recently proposed a method called Material Fingerprinting for the rapid discovery of mechanical material models that avoids solving continuous optimization problems. Material Fi…

cs.RO2026

Infinite-Dimensional Closed-Loop Inverse Kinematics for Soft Robots via Neural Operators

Carina Veil, Moritz Flaschel, Ellen Kuhl +1

For fully actuated rigid robots, kinematic inversion is a purely geometric problem, efficiently solved by closed-loop inverse kinematics (CLIK) schemes that compute joint velocitie…

cs.CE2026★ 2 cited

Unsupervised Material Fingerprinting: Ultra-fast hyperelastic model discovery from full-field experimental measurements

Moritz Flaschel, Miguel Angel Moreno-Mateos, Simon Wiesheier +2

Material Fingerprinting is a lookup table-based strategy to discover material models from experimental measurements, which completely avoids the need to solve an optimization probl…

cs.RO2025

Shape-Space Graphs: Fast and Collision-Free Path Planning for Soft Robots

Carina Veil, Moritz Flaschel, Ellen Kuhl

Soft robots, inspired by elephant trunks or octopus arms, offer extraordinary flexibility to bend, twist, and elongate in ways that rigid robots cannot. However, their motion plann…

cs.CE2025

Material Fingerprinting: A shortcut to material model discovery without solving optimization problems

Moritz Flaschel, Denisa Martonová, Carina Veil +1

We propose Material Fingerprinting, a new method for the rapid discovery of mechanical material models from direct or indirect data that avoids solving potentially non-convex optim…

cs.CE2025

Non-smooth optimization meets automated material model discovery

Moritz Flaschel, Trevor Hastie, Ellen Kuhl

Automated material model discovery disrupts the tedious and time-consuming cycle of iteratively calibrating and modifying manually designed models. Non-smooth L1-norm regularizatio…