264 citations · 1.5k across the 33 of their papers we have counts for
34 papers
An associated model of Coulomb friction based on the real area of contact
Manon Thbaut, Laura De Lorenzis
Coulomb friction is a non-associated law: it contains a threshold that depends on the contact pressure, i.e. a stress that is not conjugate to any internal variable, hence it admit…
On phase-field regularization in dynamic fracture with brittle and cohesive formulations
Jonas Heinzmann, Francesco Vicentini, Pietro Carrara +1
Phase-field models of fracture are widely used for simulating crack nucleation and propagation, yet the role of the phase-field regularization in the dynamic regime is not fully un…
Imposing Boundary Conditions on Neural Operators via Learned Function Extensions
Sepehr Mousavi, Siddhartha Mishra, Laura De Lorenzis
Neural operators have emerged as powerful surrogates for the solution of partial differential equations (PDEs), yet their ability to handle general, highly variable boundary condit…
Variational phase-field modeling of fracture and fatigue in shape memory alloys: a one-dimensional study
Alma Brambilla, Laura De Lorenzis, Lorenza Petrini
We propose a novel variational phase-field model for fracture and fatigue in pseudoelastic shape memory alloys (SMAs). The model, developed in a one-dimensional setting, builds upo…
Iterative convergence in phase-field brittle fracture computations: exact line search is all you need
Jonas Heinzmann, Francesco Vicentini, Pietro Carrara +1
Variational phase-field models of brittle fracture pose a local constrained minimization problem of a non-convex energy functional. In the discrete setting, the problem is most oft…
Lattice Boltzmann for linear elastodynamics: periodic problems and Dirichlet boundary conditions
Oliver Boolakee, Martin Geier, Laura De Lorenzis
We propose a new second-order accurate lattice Boltzmann formulation for linear elastodynamics that is stable for arbitrary combinations of material parameters under a CFL-like con…
Calibration and Validation of a Phase-Field Model of Brittle Fracture within the Damage Mechanics Challenge
Jonas Heinzmann, Pietro Carrara, Chenyi Luo +6
In the context of the Damage Mechanics Challenge, we adopt a phase-field model of brittle fracture to blindly predict the behavior up to failure of a notched three-point-bending sp…
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…
Phase-Field Modeling of Fracture with Physics-Informed Deep Learning
M. Manav, R. Molinaro, S. Mishra +1
We explore the potential of the deep Ritz method to learn complex fracture processes such as quasistatic crack nucleation, propagation, kinking, branching, and coalescence within t…
An adaptive acceleration scheme for phase-field fatigue computations
Jonas Heinzmann, Pietro Carrara, Marreddy Ambati +2
Phase-field models of fatigue are capable of reproducing the main phenomenology of fatigue behavior. However, phase-field computations in the high-cycle fatigue regime are prohibit…
The Language of Hyperelastic Materials
Georgios Kissas, Siddhartha Mishra, Eleni Chatzi +1
The automated discovery of constitutive laws forms an emerging research area, that focuses on automatically obtaining symbolic expressions describing the constitutive behavior of s…
Dehydration drives damage in the freezing of brittle hydrogels
Shaohua Yang, Dominic Gerber, Yanxia Feng +6
It is widely known that freezing breaks soft, wet materials. However, the mechanism underlying this damage is still not clear. To understand this process, we freeze model, brittle…
Enhanced Floating Isogeometric Analysis
Helge C. Hille, Siddhant Kumar, Laura De Lorenzis
The numerical simulation of additive manufacturing techniques promises the acceleration of costly experimental procedures to identify suitable process parameters. We recently propo…
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…
Neural Cellular Automata for Solidification Microstructure Modelling
Jian Tang, Siddhant Kumar, Laura De Lorenzis +1
We propose Neural Cellular Automata (NCA) to simulate the microstructure development during the solidification process in metals. Based on convolutional neural networks, NCA can le…
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,…
A new lattice Boltzmann scheme for linear elastic solids: periodic problems
Oliver Boolakee, Martin Geier, Laura De Lorenzis
We propose a new second-order accurate lattice Boltzmann scheme that solves the quasi-static equations of linear elasticity in two dimensions. In contrast to previous works, our fo…
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…
Phase-field modeling of brittle fracture in heterogeneous bars
Francesco Vicentini, Pietro Carrara, Laura De Lorenzis
We investigate phase-field modeling of brittle fracture in a one-dimensional bar featuring a continuous variation of elastic and/or fracture properties along its axis. Our main goa…
Floating Isogeometric Analysis
Helge C. Hille, Siddhant Kumar, Laura De Lorenzis
We propose Floating Isogeometric Analysis (FLIGA), which extends the concepts of IGA to Lagrangian extreme deformation analysis. The method is based on a novel tensor-product const…