Things that I've been thinking about
My research interest lie at the intersection of deep learning, dynamical systems, and computational modeling. Specifically, I'm interested in invastigating how we can use deep learning approaches to improve computational modeling of physical, engineering, and biological systems.
Deep Learning + Computational Modeling
Learning nonlinear reduced order models
Many fields, particularly in the physical sciences, are in the fortunate position of having models based on first-principles that describe the evolution of certain systems with near-perfect accuracy. Notable examples include the Navier-Stokes equations in fluid mechanics or Schrödinger's equation in quantum mechanics. Although in principle it is possible to numerically solve these equations through direct numerical simulations, this often yields systems of equations with millions or billions of degrees of freedom. Even with recent advances in computational power and memory capacity solving these high-fidelity models is still computationally intractable for multi-query and time-critical applications such as design optimization, uncertainty quantification, and model predictive control.
Although there are a wide variety of principled strategies for constructing reduced order models from data, most are intrusive as they require access to the system operators. Futher, some systems may require special treatment of nonlinearities to ensure computational efficiency or additional modeling to preserve stability. The recent rise in deep learning and big data have driven a shift in the way complex spatiotemporal systems are modeled. In a recent work titled Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems we develop a deep learning based, completely data-driven approach to model reduction. At the core of this approach is an extension of projection-based model reduction in which a low-dimensional representation of the high-dimensional data is learned in the form of coordinates on an expressive nonlinear manifold. The dynamics of this representation on the underlying manifold are also learned using a representative collection of solution snapshots.
Deep dilation models for multi-scale dynamics
Recurrent neural networks (RNNs) have long been used to model sequential or time-dependent data. However, many real-world physical systems, e.g., turbulent flow in fluids, exhibit multi-scale dynamics. Thus, while RNNs can accurately model the dynamics of a system at one time-scale, they fail to capture the dynamics occuring over a multitude of scales. This problem is not unique to physical systems: RNNs also fail to capture the varying time-scales evident in speech data. Recent work involving models with dilated RNNs and convolutional networks, e.g., WaveNet (see figure), have shown great performance in modeling multi-scale speech data. Currently, I am developing deep dilation models inspired by WaveNet to help improve deep learning based modeling of multi-scale physical systems. This work attempts to explore the how dilated RNNs help capture dynamics at different scales, and thereby significantly increasing the applicability of deep learning based modeling approaches to real-world dynamical systems.
Quinoa: Adapative Computational Fluid Dynamics
As we enter the exascale era of high-performance computing performance of computational physics codes will increasingly depend on their ability to asynchronously adapt to varying computational loads induced by multi-scale and multi-physics simulations as well as varying hardware performance. During my time at Los Alamos National Laboratory I contributed to the development of Quinoa, an open-source computational science code specifically addressing the challenges that will be faced with heterogeneous exascale machines. Quinoa is built on top of the Charm++ runtime system which allows for asynchronous parallel execution enabling the overlapping of computation, communication, and I/O. In future work, I would like to incorporate deep learning based methods into Quinoa for asynchronous parallel modeling based solely on multi-scale and multi-physics data.