Engineering topics

Chen, Yuming

Publications and source records attributed to Chen, Yuming.

3 records

DOE OSTI2023

Reduced-order autodifferentiable ensemble Kalman filters

This paper introduces a computational framework to reconstruct and forecast a partially observed state that evolves according to an unknown or expensive-to-simulate dynamical system. Our reduced-order autodifferentiable ensemble Kalman filters (ROAD-EnKFs) learn a latent low-dimensional surrogate model for the dynamics and a decoder that maps from the latent space to the state space. The learned dynamics and decoder are then used within an EnKF to reconstruct and forecast the state. Numerical experiments show that if the state dynamics exhibit a hidden low-dimensional structure, ROAD-EnKFs achieve higher accuracy at lower computational cost compared to existing methods. If such structure is not expressed in the latent state dynamics, ROAD-EnKFs achieve similar accuracy at lower cost, making them a promising approach for surrogate state reconstruction and forecasting.

Mathematics↗

DOE OSTI2022

Methods and apparatus to facilitate alkali metal transport during battery cycling, and batteries incorporating same

An anode includes a mixed ionic-electronic conductor (MIEC) with an open pore structure. The open pore structure includes open pores to facilitate motion of an alkali metal into and/or out of the MIEC. The open pore structure thus provides open space to relieve the stresses generated by the alkali metal when charging/discharging a battery. The MIEC is formed from a material that is thermodynamically and electrochemically stable against the alkali metal to prevent the formation of solid-electrolyte interphase (SEI) debris and the formation of dead alkali metal. The MIEC may also be passive (the MIEC does not store or release alkali metal). In one example, the open pore structure may be an array of substantially aligned tubules with a width less than about 300 nm, a wall thickness between about 1 nm to about 30 nm, and a height of at least 10 um arranged as a honeycomb.

Li, Ju↗

DOE OSTI2022

Autodifferentiable Ensemble Kalman Filters

Data assimilation is concerned with sequentially estimating a temporally evolving state. This task, which arises in a wide range of scientific and engineering applications, is particularly challenging when the state is high-dimensional and the state-space dynamics are unknown. This paper introduces a machine learning framework for learning dynamical systems in data assimilation. Here, our auto-differentiable ensemble Kalman filters (AD-EnKFs) blend ensemble Kalman filters for state recovery with machine learning tools for learning the dynamics. In doing so, AD-EnKFs leverage the ability of ensemble Kalman filters to scale to high-dimensional states and the power of automatic differentiation to train high-dimensional surrogate models for the dynamics. Numerical results using the Lorenz-96 model show that AD-EnKFs outperform existing methods that use expectation-maximization or particle filters to merge data assimilation and machine learning. In addition, AD-EnKFs are easy to implement and require minimal tuning.

autodifferentiation↗