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 > Learning Nonlocal Constitutive PDE Models with Vector-Cloud Neural Networks

## Learning Nonlocal Constitutive PDE Models with Vector-Cloud Neural NetworksAdd to your list(s) Download to your calendar using vCal - Jiequn Han (Princeton University)
- Thursday 18 November 2021, 16:30-17:00
- Seminar Room 1, Newton Institute.
If you have a question about this talk, please contact nobody. MDLW03 - Deep learning and partial differential equations Constitutive models are widely used for modeling complex systems in science and engineering, when first-principle-based, well-resolved simulations are prohibitively expensive. For example, in fluid dynamics, constitutive models are required to describe nonlocal, unresolved physics such as turbulence and laminar-turbulent transition. However, traditional constitutive models based on PDEs often lack robustness and are too rigid to accommodate diverse calibration datasets. We propose a frame-independent, nonlocal constitutive model based on a vector-cloud neural network that represents the physics of PDEs and meanwhile can be learned with data. The model predicts the closure variable at a point based on the flow information in its neighborhood. Such nonlocal information is represented by a group of points, each having a feature vector attached to it, and thus the input is referred to as vector cloud. The cloud is mapped to the closure variable through a frame-independent neural network, invariant both to coordinate translation and rotation and to the ordering of points in the cloud. As such, the network can deal with any number of arbitrarily arranged grid points and thus is suitable for unstructured meshes in fluid simulations. The merits of the proposed network are demonstrated for scalar transport PDEs on a family of parameterized periodic hill geometries. The vector-cloud neural network is a promising tool not only as nonlocal constitutive models and but also as general surrogate models for PDEs on irregular domains. 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 listsWolfson College Education Society Cambridge Business Lectures Cambridge Cell Cycle Club Talks## Other talksUniversal NHS healthcare: has the pandemic changed the promise? Gateway Advisory Board Evaluation of Semantic Answer Similarity Metrics Passive brain tumour visualisation during surgery using computer vision Finite mutation type in presence of coefficients |