All Events

Apr 29

AM Seminar: Uncertainty Quantification and Machine Learning of Physical Laws Hidden Behind Noisy Data

Guang Lin
Associate Professor

Abstract In this talk, I will present a new data-driven paradigm on how to quantify structural uncertainty (model-form uncertainty) and learn physical laws hidden behind noisy data in complex systems governed by partial differential equations (PDEs). The key idea is to identity and approximate... Read More

Apr 22

AM Seminar: Hidden Physics Models. Machine Learning of Non-Linear Partial Differential Equations

Maziar Raissi

Abstract A grand challenge with great opportunities is to develop a coherent framework that enables blending conservation laws, physical principles, and/or phenomenological behaviors expressed by differential equations with the vast data sets available in many fields of engineering, science, and... Read More

Apr 15

AM Seminar: Data-driven modeling of stochastic systems using physics-aware deep learning

Paris Perdikaris
Assistant Professor

Abstract: We present a probabilistic deep learning methodology that enables the construction of predictive data-driven surrogates for stochastic systems. Leveraging recent advances in variational inference, we put forth a scalable computational framework for discovering surrogate models from... Read More

Pages