COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |

University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Generative hyperplasticity with physics-informed probabilistic diffusion fields

## Generative hyperplasticity with physics-informed probabilistic diffusion fieldsAdd to your list(s) Download to your calendar using vCal - Adrian Buganza Tepole (Purdue University)
- Monday 31 July 2023, 11:15-12:15
- Seminar Room 1, Newton Institute.
If you have a question about this talk, please contact nobody. USMW02 - Mathematical mechanical biology: old school and new school, methods and applications Complex materials such as soft tissues exhibit nonlinear anisotropic response and hetergeneous mechanical properties. Data-driven methods have been recently developed to capture the rich mechanical behavior of these materials under extreme deformations. In particular, we have contributed to the field by leveraging neural ordinary differential equations (NODEs) as the building blocks of strain energy density functions that automatically satisfy polyconvexity, objectivity, material symmetry and positive energy dissipation requirements for realistic and physically plausible material models. However, these data-driven models have lacked consideration of uncertainty. This is particularly problematic for soft tissues which exhibit a large variation in mechanical properties from one individual to another. Here we establish a generative modeling framework based on stable diffusion to model distributions of materials while satisfying physics constraints. We use NOD Es to describe the material response. Because the NODE framework automatically satisfies the desired physics, any samples of parameters of the NODE produces feasible materials. For a given material of interest e.g. skin, we assume that stress-strain curves from the population are available. Fitting a subset of the NODE parameters to the stress-strain data yields samples over the parameter space of the NOD Es. Diffusion probabilistic models are then employed to learn that distribution over these NODE parameters and, inplicitly, over the constitutive models. We showcase the ability of the framework to learn the distribution of material behavior for both syntethic examples and murine skin data, outperforming standard density estimation techniques. We anticipate that this work will further establish the use of data-driven methods for materials that exhibit large variation across a population for which uncertainty quantification is essential. Co-authors: Vahidullah Tac, Manuel Rausch, Ilias Bilionis, Francisco Sahli Costabal This talk is part of the Isaac Newton Institute Seminar Series series. ## This talk is included in these lists:- All CMS events
- Featured lists
- INI info aggregator
- Isaac Newton Institute Seminar Series
- School of Physical Sciences
- Seminar Room 1, Newton Institute
- bld31
Note that ex-directory lists are not shown. |
## Other listsCambridge Area Sequencing Informatics Meeting VI (2014) CaMedia History & Philosophy of Science @ the Cavendish## Other talksGroup Work Group Work Some analytic questions arising in quantum information The challenges of ocean navigation Lunch at Moller Institute Can AI probe toric Calabi-Yau? |