Stanford Engineering
Living Matter Lab

welcome to the living matter lab!

we integrate physics-based modeling with machine learning and create interactive simulation tools to understand, explore, and predict the dynamics of living systems

discovering the mechanics of artificial meat

we explore artificial meat using tension, compression, and shear tests and discover the model and parameters that best explain its behavior to inform the design of more authentic meat substitutes that are sustainable and environmentally friendly while still offering the familiar taste and texture of traditional meat

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automated model discovery for soft matter

we challenge the general belief that neural networks can teach us nothing about the physics of a material. we reverse engineer a new family of constitutive artificial neural networks that have the potential to induce a paradigm shift in constitutive modeling, from user-defined model selection to automated model discovery

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soft robotics inspired by the elephant trunk

inspired by nature, we model, simulate, and build soft slender actuators for programmable multimodal deformation using the theory of active filaments, machine learning, and liquid crystal elastomers that activate a soft matter system by joule heating to guide the design and control of soft robotic systems

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democratizing simulation through automation

we democratize constitutive modeling through automated model discovery, embedded in a universal material subroutine, to make scientific simulations accessible to a more inclusive and diverse community, and accelerate the design of new functional materials with tailored properties

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discovering knowledge from massive data

we discover interpretable and predictive models that provide simple relationships among scientific variables by integrating Lp regularization for subset selection with constitutive neural networks, and induce sparsity by a hybrid approach that combines controlled regularization and physical constraints

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automated model discovery for human brain

we propose a new strategy to simultaneously discover both model and parameters that best characterize the behavior of human gray and white matter tissue. our discovered model outperforms existing models in tension, compression, and shear tests

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integrating bayesian inference, neural networks, and physics

we integrate data, physics, and uncertainties by combining neural networks, physics informed modeling, and bayesian inference to improve the predictive potential of traditional neural network models.

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bayesian analysis of misfolded tau

amyloid-beta drives tau pathology

we personalize a network diffusion model using longitudinal tau pet data of 76 subjects and apply bayesian inference with hierarchical priors to identify correlations between amyloid beta and tau and infer personalized tau diffusion and production rates

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effect of drugs

sex matters!

we integrate multiscale modeling and machine learning to gain mechanistic insight into the sex-specific origin of drug-induced cardiac arrhythmias and show that sex differences in ion channel activity, tissue conductivity, and heart dimensions put females at higher arrhythmogenic risk than males

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can we reverse engineer an elephant trunk?

we explore the active filament theory that efficiently correlates fiber stretch and orientation to the intrinsic curvature of slender structures to robustly solve inverse problems in soft robotics inspired by natural manipulators such as the elephant trunk

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sequence of steps towards personalized cardiac medicine

precision medicine in human heart modeling

with a view towards precision medicine, we integrate human heart electrophysiology, solid mechanics, and fluid dynamics and explore clinical applications in drug development, pacing lead failure, heart failure, ventricular assist devices, and mitral valve repair

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