264 citations · 926 across the 20 of their papers we have counts for
21 papers
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…
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…
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…
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…
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…
Discovery of Hyperelastic Constitutive Laws from Experimental Data with EUCLID
Arefeh Abbasi, Maurizio Ricci, Pietro Carrara +4
We assess the performance of EUCLID, Efficient Unsupervised Constitutive Law Identification and Discovery, a recently proposed framework for automated discovery of constitutive law…
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…
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…
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…
A topology optimisation framework to design test specimens for one-shot identification or discovery of material models
Saeid Ghouli, Moritz Flaschel, Siddhant Kumar +1
The increasing availability of full-field displacement data from imaging techniques in experimental mechanics is determining a gradual shift in the paradigm of material model calib…
A review on data-driven constitutive laws for solids
Jan Niklas Fuhg, Govinda Anantha Padmanabha, Nikolaos Bouklas +6
This review article highlights state-of-the-art data-driven techniques to discover, encode, surrogate, or emulate constitutive laws that describe the path-independent and path-depe…
Reduced and All-at-Once Approaches for Model Calibration and Discovery in Computational Solid Mechanics
Ulrich Römer, Stefan Hartmann, Jendrik-Alexander Tröger +4
In the framework of solid mechanics, the task of deriving material parameters from experimental data has recently re-emerged with the progress in full-field measurement capabilitie…
Data-driven methods for quantitative imaging
Guozhi Dong, Moritz Flaschel, Michael Hintermüller +3
In the field of quantitative imaging, the image information at a pixel or voxel in an underlying domain entails crucial information about the imaged matter. This is particularly im…
Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID
Moritz Flaschel, Huitian Yu, Nina Reiter +5
We propose an automated computational algorithm for simultaneous model selection and parameter identification for the hyperelastic mechanical characterization of human brain tissue…
Single-test evaluation of directional elastic properties of anisotropic structured materials
Jagannadh Boddapati, Moritz Flaschel, Siddhant Kumar +2
When the elastic properties of structured materials become direction-dependent, the number of their descriptors increases. For example, in two-dimensions, the anisotropic behavior…
Automated identification of linear viscoelastic constitutive laws with EUCLID
Enzo Marino, Moritz Flaschel, Siddhant Kumar +1
We extend EUCLID, a computational strategy for automated material model discovery and identification, to linear viscoelasticity. For this case, we perform a priori model selection…
Automated discovery of generalized standard material models with EUCLID
Moritz Flaschel, Siddhant Kumar, Laura De Lorenzis
We extend the scope of our approach for unsupervised automated discovery of material laws (EUCLID) to the case of a material belonging to an unknown class of behavior. To this end,…
NN-EUCLID: deep-learning hyperelasticity without stress data
Prakash Thakolkaran, Akshay Joshi, Yiwen Zheng +3
We propose a new approach for unsupervised learning of hyperelastic constitutive laws with physics-consistent deep neural networks. In contrast to supervised learning, which assume…
Bayesian-EUCLID: discovering hyperelastic material laws with uncertainties
Akshay Joshi, Prakash Thakolkaran, Yiwen Zheng +4
Within the scope of our recent approach for Efficient Unsupervised Constitutive Law Identification and Discovery (EUCLID), we propose an unsupervised Bayesian learning framework fo…
Discovering plasticity models without stress data
Moritz Flaschel, Siddhant Kumar, Laura De Lorenzis
We propose a new approach for data-driven automated discovery of material laws, which we call EUCLID (Efficient Unsupervised Constitutive Law Identification and Discovery), and we…
Unsupervised discovery of interpretable hyperelastic constitutive laws
Moritz Flaschel, Siddhant Kumar, Laura De Lorenzis
We propose a new approach for data-driven automated discovery of isotropic hyperelastic constitutive laws. The approach is unsupervised, i.e., it requires no stress data but only d…