We study fluid dynamics and heat transfer in complex natural phenomena and engineering systems using numerical, mathematical, and statistical models, guided by observational and experimental data. Our work is often motivated by theoretical and applied problems related to environment and energy. Examples of problems of interest are environmental and geophysical flows, reduced-order modeling, extreme weather events, atmospheric turbulence, climate modeling, flow control in energy systems, and numerical and mathematical modeling of thermo-fluid processes. Our research is currently supported by NASA, NSF, ONR, National Academy of Sciences, Microsoft AI, Mitsubishi Electric Research Lab, Rice University Creative Ventures, and Rice Houston Engagement and Recovery Effort.
Posted May 2020: A postdoc/scientist position is available to work on using machine learning and mixed-precision computing for modeling of subgrid processes in geophysical turbulence and weather/climate system. See the details here.
- April 2020: Prof. Hassanzadeh has received the Young Investigator Award from the Office of Naval Research (ONR) for a proposal entitled “Using Artificial Intelligence and Inexact Computing to Improve Modeling of Multi-scale, Multi-physics, Chaotic Dynamical Systems with Applications to Weather Predictions”. Check out the Rice press release.
- March 2020: AGU’s EOS has a news story “Combining AI and Analog Forecasting to Predict Extreme Weather” covering our recent paper in JAMES on this topic.
- February 2020: Two papers on using deep learning for pattern recognition-based weather prediction, both led by PhD student Ashesh Chattopadhyay are out. Analog forecasting of extreme‐causing weather patterns using deep learning is published in AGU’s Journal of Advances in Modeling Earth System and Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spatio-temporal climate data is published in Scientific Reports. Check out the Rice press release.
- December 2019: Our recent paper on the projected increase in the size of blocking events under climate change is highlighted in NSF Research News and in a short video clip by pattern.com.
- November 2019: Our paper led by Ph.D. student Ebrahim Nabizadeh and in collaboration with Prof. Da Yang (UC Davis/LBNL) and Prof. Libby Barnes (CSU) is published in Geophysical Research Letters. In this paper, we show that jet streams’ blocking events, which cause weather extremes such as heat waves and cold spells, are becoming larger under climate change. We also use the Buckingham-pi theorem to derive a scaling law for the size of the blocking events in a hierarchy of climate models. The findings have implications for the size and magnitude of future extreme weather events. See the Rice press release, NSF Research News, phys.org & ScienceDaily
- November 2019: Our ongoing work on predicting extreme weather events using deep learning is highlighted as one of the research activities at Rice focused on using AI to address important, societal challenges.
- November 2019: Prof. Hassanzadeh gave a talk UC Berkeley Fluids Seminar entitled “Data-driven modeling of geophysical turbulence using Koopman-based Fluctuation-Dissipation Theorem”.
- November 2019: Prof. Hassanzadeh gave two talks at the Naval Academy Mathematics Department entitled “Data-driven modeling of multi-scale, multi-physics dynamical systems using deep learning for improved weather/climate predictions” and “Data-driven modeling of geophysical turbulence using statistical physics + dynamical systems”.
- October 2019: Prof. Steve Brunton from the University of Washington visited our group and gave a talk entitled “Machine Learning and Sparse Optimization for Modeling, Sensing, and Controlling Fluid Dynamics” at the MECH seminar series.
- October 2019: Prof. Kamran Alba from the University of Houston visited our group and gave a talk entitled “Particle-laden Exchange Flows: Experiment and Theory” at the MECH seminar series.
- June 2019: Second-year PhD students Ashesh and Charles presented our recent work on using a hierarchy of deep learning techniques for data-driven prediction of a chaotic dynamical system at the ICML workshop “Climate change: How can AI help?“.
- May 2019: Our paper led by Ph.D. student Packard Chan and Prof. Zhiming Kuang (Harvard University) is published in Geophysical Research Letters. In this paper, we evaluate/compare indices for atmospheric blocking events in terms of how well they capture heat wave-causing weather patterns.