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Deng, Sili

Publications and source records attributed to Deng, Sili.

3 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Ultrafast materials synthesis and manufacturing techniques for emerging energy and environmental applications

Energy and environmental issues have attracted increasing attention globally, where sustainability and low-carbon emissions are seriously considered and widely accepted by government officials. In response to this situation, the development of renewable energy and environmental technologies is urgently needed to complement the usage of traditional fossil fuels. While a big part of advancement in these technologies relies on materials innovations, new materials discovery is limited by sluggish conventional materials synthesis methods, greatly hindering the advancement of related technologies. To address this issue, this review introduces and comprehensively summarizes emerging ultrafast materials synthesis methods that could synthesize materials in times as short as nanoseconds, significantly improving research efficiency. We discuss the unique advantages of these methods, followed by how they benefit individual applications for renewable energy and the environment. We also highlight the scalability of ultrafast manufacturing towards their potential industrial utilization. Finally, we provide our perspectives on challenges and opportunities for the future development of ultrafast synthesis and manufacturing technologies. Here, we anticipate that fertile opportunities exist not only for energy and the environment but also for many other applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Stiff neural ordinary differential equations

Neural Ordinary Differential Equations (ODEs) are a promising approach to learn dynamical models from time-series data in science and engineering applications. This work aims at learning neural ODEs for stiff systems, which are usually raised from chemical kinetic modeling in chemical and biological systems. We first show the challenges of learning neural ODEs in the classical stiff ODE systems of Robertson’s problem and propose techniques to mitigate the challenges associated with scale separations in stiff systems. We then present successful demonstrations in stiff systems of Robertson’s problem and an air pollution problem. The demonstrations show that the usage of deep networks with rectified activations, proper scaling of the network outputs as well as loss functions, and stabilized gradient calculations are the key techniques enabling the learning of stiff neural ODEs. The success of learning stiff neural ODEs opens up possibilities of using neural ODEs in applications with widely varying time-scales, such as chemical dynamics in energy conversion, environmental engineering, and life sciences.

97 MATHEMATICS AND COMPUTING↗