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

what about fungi?
we observe a strong correlation between mechanical tests and sensory surveys, and perceive fungi steak as more moist and more fibrous than animal and plant-based meats. fungi steak is an attractive, structurally equivalent, and sensorially superior alternative protein source that is healthier for people and for the planet.

inspired by nature?
with more than 90,000 muscle fascicles, the elephant trunk is a complex biological structure that is fascinating scientists across all disciplines. we design reduced-order models of the elephant trunk that can, within a fraction of a second, predict the trunk's motion as a result of its muscular activity.

discovering uncertainty
understanding uncertainty is critical, especially when data are sparse and variations are large. we integrate concepts of Bayesian learning and constitutive neural networks to discovery interpretable models, parameters, and uncertainties that best explain soft matter systems

a universal material subroutine
we design a universal material subroutine that automatically integrates novel constitutive models of varying complexity into non-linear finite element programs, and empowers all users--not just domain experts--to perform reliable engineering analyses of soft matter systems

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

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.

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
in the news
- improving plant-based meats national science foundation news
- can AI improve plant-based meats? stanford report
- the mechanical and sensory signature of plant-based and animal meat behind the paper
- the telltale heart washington post feature about the living heart project
- women’s heart disease is underdiagnosed, but new AI models can help remedy this lab manager
- new models improve heart disease risk prediction, especially for women news medical
- AI offers paradigm shift in study of brain injury stanford | news
- this week in AI TechCrunch
- can computational modeling help us understand Alzheimer's disease? the future of everything
- college campuses are COVID-19 superspreaders? US news & world report
- students develop computer models to test return-to-campus strategies stanford engineering
- the newfoundland story atlantic ctv news cbc radio canada
- how effective are travel bans? swiss public radio welt
- passionate scientist and triathlete interview
- stanford-led team simulates how alzheimer’s disease spreads through the brain stanford report