arXiv subjects

Skyler R. St. Pierre

Publications and source records attributed to Skyler R. St. Pierre.

3 recordsLinked to original sources

arXiv2026

Emergent symmetry in mushroom-based foods

Mushroom-based foods exhibit anisotropic fibrous microstructures formed by networks of hyphae and represent a unique class of structured soft matter. These materials provide an opportunity to probe a fundamental mechanics question: does anisotropic structure translate into anisotropic constitutive symmetry? Here we combine directional tension, compression, and shear experiments with automated model discovery and sensory evaluation to investigate three mushroom-based foods with distinct microstructural architectures: mycelium, fruiting body, and a protein-mycelium blend. We show that these three materials exhibit distinct degrees of directional organization, yet span a broad spectrum of constitutive symmetry classes, from strongly anisotropic to effectively isotropic behavior. Sensory evaluation reveals a similar progression, while perceived fibrousness remains largely independent of directional stiffness. Automated model discovery introduces fiber-dependent invariants only when required by the data and directly identifies the governing symmetry class from experiments. Our results show that visual appearance alone does not uniquely determine material symmetry; instead, constitutive symmetry depends on the statistically relevant descriptors of the microstructure. These findings establish mushroom-based foods as a model system to study emergent symmetries in structured soft matter and provide a general framework to discover constitutive symmetry directly from experimental data.

cs.CE↗

arXiv2026

Artificial Intelligence for Food Innovation

Global food systems must deliver nutritious, sustainable foods while sharply reducing environmental impact. Yet, food innovation remains slow, empirical, and fragmented. Artificial intelligence (AI) offers a transformative path to link molecular composition to functional performance, connect chemical structure to sensory outcomes, and accelerate cross-disciplinary innovation across the production pipeline. While broadly applicable to food systems, we focus on sustainable proteins--plant-based, fermentation-derived, and cultivated--as a high-impact testbed for AI-driven closed-loop design. We review the applications, opportunities, and challenges of AI for Food as an emerging discipline that integrates ingredient design, formulation development, fermentation and production, texture analysis, sensory science, manufacturing, and recipe generation. We identify four priorities: advancing scientific machine learning with embedded domain priors, treating food as a programmable biomaterial, building self-driving laboratories for automated discovery, and developing deep reasoning models that integrate nutrition and sustainability. Integrating AI responsibly into the food innovation cycle can accelerate the transition to sustainable food systems and establish a predictive, design-driven science of food for human and planetary health.

cs.CE↗

arXiv2024

On sparse regression, Lp-regularization, and automated model discovery

Sparse regression and feature extraction are the cornerstones of knowledge discovery from massive data. Their goal is to discover interpretable and predictive models that provide simple relationships among scientific variables. While the statistical tools for model discovery are well established in the context of linear regression, their generalization to nonlinear regression in material modeling is highly problem-specific and insufficiently understood. Here we explore the potential of neural networks for automatic model discovery and induce sparsity by a hybrid approach that combines two strategies: regularization and physical constraints. We integrate the concept of Lp regularization for subset selection with constitutive neural networks that leverage our domain knowledge in kinematics and thermodynamics. We train our networks with both, synthetic and real data, and perform several thousand discovery runs to infer common guidelines and trends: L2 regularization or ridge regression is unsuitable for model discovery; L1 regularization or lasso promotes sparsity, but induces strong bias; only L0 regularization allows us to transparently fine-tune the trade-off between interpretability and predictability, simplicity and accuracy, and bias and variance. With these insights, we demonstrate that Lp regularized constitutive neural networks can simultaneously discover both, interpretable models and physically meaningful parameters. We anticipate that our findings will generalize to alternative discovery techniques such as sparse and symbolic regression, and to other domains such as biology, chemistry, or medicine. Our ability to automatically discover material models from data could have tremendous applications in generative material design and open new opportunities to manipulate matter, alter properties of existing materials, and discover new materials with user-defined properties.

cs.LG↗