113 citations · 226 across the 11 of their papers we have counts for
12 papers
Brilliance: toward autonomous computational research in particle accelerator physics
Jinyu Wan, Yu Zhao, Wei Bao +4
Computational research in physics requires continuous interplay between simulation and scientific interpretation, demanding substantial specialist effort. Here we introduce Brillia…
Artificial Intelligence and the Generative Science of Food Formulation
Vahidullah Tac, Ellen Kuhl
Food formulation requires balancing taste, nutrition, sustainability, and cost. Traditionally, new foods have emerged through empirical experimentation, expert intuition, and itera…
Generative AI for material design: A mechanics perspective from burgers to matter
Vahidullah Tac, Ellen Kuhl
Generative artificial intelligence offers a new paradigm to design matter in high-dimensional spaces. However, its underlying mechanisms remain difficult to interpret and limit ado…
Texture Independently Drives Liking in AI-Generated Alternative Protein Burgers
Vahidullah Tac, Aeneas O. Koosis, Ellen Kuhl
Texture shapes how we perceive and like food, yet clear links between mechanical measurements and sensory perception of texture remain elusive. Here we combine sensory data from a…
Generative Artificial Intelligence creates delicious, sustainable, and nutritious burgers
Vahidullah Tac, Christopher Gardner, Ellen Kuhl
Food choices shape both human and planetary health; yet, designing foods that are delicious, nutritious, and sustainable remains challenging. Here we show that generative artificia…
Fully data-driven inverse hyperelasticity with hyper-network neural ODE fields
Vahidullah Taç, Amirhossein Amiri-Hezaveh, Manuel K. Rausch +3
We propose a new framework for identifying mechanical properties of heterogeneous materials without a closed-form constitutive equation. Given a full-field measurement of the displ…
Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields
Vahidullah Tac, Manuel K Rausch, Ilias Bilionis +2
Many natural materials exhibit highly complex, nonlinear, anisotropic, and heterogeneous mechanical properties. Recently, it has been demonstrated that data-driven strain energy fu…
Benchmarks for physics-informed data-driven hyperelasticity
Vahidullah Tac, Kevin Linka, Francisco Sahli-Costabal +2
Data-driven methods have changed the way we understand and model materials. However, while providing unmatched flexibility, these methods have limitations such as reduced capacity…
Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations
Vahidullah Tac, Manuel K. Rausch, Francisco Sahli-Costabal +1
We develop a fully data-driven model of anisotropic finite viscoelasticity using neural ordinary differential equations as building blocks. We replace the Helmholtz free energy fun…
Data-driven Tissue Mechanics with Polyconvex Neural Ordinary Differential Equations
Vahidullah Tac, Francisco S. Costabal, Adrian Buganza Tepole
Data-driven methods are becoming an essential part of computational mechanics due to their unique advantages over traditional material modeling. Deep neural networks are able to le…
Data-driven Modeling of the Mechanical Behavior of Anisotropic Soft Biological Tissue
Vahidullah Tac, Vivek D. Sree, Manuel K. Rausch +1
Constitutive models that describe the mechanical behavior of soft tissues have advanced greatly over the past few decades. These expert models are generalizable and require the cal…
Predicting the Mechanical Properties of Biopolymer Gels Using Neural Networks Trained on Discrete Fiber Network Data
Yue Leng, Vahidullah Tac, Sarah Calve +1
Biopolymer gels, such as those made out of fibrin or collagen, are widely used in tissue engineering applications and biomedical research. Moreover, fibrin naturally assembles into…