264 citations · 1.3k across the 16 of their papers we have counts for
16 papers
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…
Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling
Li Zheng, Konstantinos Karapiperis, Siddhant Kumar +1
The rise of machine learning has fueled the discovery of new materials and, especially, metamaterials--truss lattices being their most prominent class. While their tailorable prope…
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,…
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…
Numerical approaches for investigating quasiconvexity in the context of Morrey's conjecture
Jendrik Voss, Robert J. Martin, Oliver Sander +3
Deciding whether a given function is quasiconvex is generally a difficult task. Here, we discuss a number of numerical approaches that can be used in the search for a counterexampl…
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…
What machine learning can do for computational solid mechanics
Siddhant Kumar, Dennis M. Kochmann
Machine learning has found its way into almost every area of science and engineering, and we are only at the beginning of its exploration across fields. Being a popular, versatile…
Data-driven modelling of scalable spinodoid structures for energy absorption
Hirak Kansara, Gary Koh, Merrin Varghese +6
The project aims to explore a novel way to design and produce cellular materials with good energy absorption and recoverability properties. Spinodoid structures offer an alternativ…
Data-driven topology optimization of spinodoid metamaterials with seamlessly tunable anisotropy
Li Zheng, Siddhant Kumar, Dennis M. Kochmann
We present a two-scale topology optimization framework for the design of macroscopic bodies with an optimized elastic response, which is achieved by means of a spatially-variant ce…
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…