Inverse electromagnetic scattering models for sea ice

Journal ArticleInverse scattering algorithms for reconstructing the physical properties of sea ice from scattered electromagnetic field data are presented. The development of these algorithms has advanced the theory of remote sensing, particularly in the microwave region, and has the potential to form the basis for a new generation of techniques for recovering sea ice properties, such as ice thickness, a parameter of geophysical and climatological importance. Moreover, the analysis underlying the algorithms has led to significant advances in the mathematical theory of inverse problems

Self-Distillation for Gaussian Process Regression and Classification

We propose two approaches to extend the notion of knowledge distillation to Gaussian Process Regression (GPR) and Gaussian Process Classification (GPC); data-centric and distribution-centric. The data-centric approach resembles most current distillation techniques for machine learning, and refits a model on deterministic predictions from the teacher, while the distribution-centric approach, re-uses the full probabilistic posterior for the next iteration. By analyzing the properties of these approaches, we show that the data-centric approach for GPR closely relates to known results for self-distillation of kernel ridge regression and that the distribution-centric approach for GPR corresponds to ordinary GPR with a very particular choice of hyperparameters. Furthermore, we demonstrate that the distribution-centric approach for GPC approximately corresponds to data duplication and a particular scaling of the covariance and that the data-centric approach for GPC requires redefining the model from a Binomial likelihood to a continuous Bernoulli likelihood to be well-specified. To the best of our knowledge, our proposed approaches are the first to formulate knowledge distillation specifically for Gaussian Process models.Comment: 10 pages; code at https://github.com/Kennethborup/gaussian_process_self_distillatio

(2021-2022 ECS Toyota Young Investigator Fellowship) Understanding and Suppression of Cation Transport into Polymer Electrolyte Membrane Fuel Cell

Polymer electrolyte membrane fuel cells (PEMFCs) are a viable zero-emissions option for the electrification of the heavy duty transportation sector. However, PEMFCs still suffer from degradation of materials over the fuel cell lifetime. Cation contaminants can be generated from corrosion of bipolar plates and balance of plant components, water contaminants, and environmental sources (e.g., Fe3+, Ca2+, Na+), making them present in the fuel or oxidant stream during operation(1). Cations have been shown to be detrimental to the performance of the PEMFC by reducing water uptake, ionic conductivity, and O2 transport, resulting in performance loss and degradation. Metal cations such as Fe3+ can also lead to chemical degradation of the membrane ionomer (2-4). It is critical to understand the mechanism and rate of cation transport from the bipolar plate channel to the membrane to develop mitigation strategy to suppress the cation transport. In this work, we present the study of the cation (Fe3+) transport mechanism through the gas diffusion layer (GDL) by introducing a cation solution in the cathode channel. Transport rates across the GDL are studied using an ex-situ GDL holder where Fe solution is introduced in the GDL substrate side with water transported through to the microporous layer side (MPL) and is collected and analyzed for Fe concentration, as shown in Figure 1a. Effect of the Fe concentration on transport rates is also studied using computational modeling. Understanding of the transport mechanism is then leveraged to identify mitigation solutions and suppress cation transport from the flow field to the electrode using a GDL with dual MPL architecture as shown in Figure 1b. Optimization of the dual MPL architecture for both durability and performance is also presented. Acknowledgements This research is supported by the 2021-2022 ECS Toyota Young Investigator fellowship and U.S. Department of Energy (DOE) Hydrogen and Fuel Cell Technologies Office, through the Million Mile Fuel Cell Truck Consortium (M2FCT). Authors acknowledge the Laboratory Directed Research and Development (LDRD) program at Los Alamos National Laboratory (LANL). References D. D. Papadias, R. K. Ahluwalia, J. K. Thomson, H. M. Meyer, M. P. Brady, H. L. Wang, J. A. Turner, R. Mukundan and R. Borup, Journal of Power Sources, 273, 1237 (2015). R. K. Ahluwalia, D. D. Papadias, N. N. Kariuki, J. K. Peng, X. P. Wang, Y. F. Tsai, D. G. Graczyk and D. J. Myers, Journal of the Electrochemical Society, 165, F3024 (2018). J. P. Braaten, X. M. Xu, Y. Cai, A. Kongkanand and S. Litster, Journal of the Electrochemical Society, 166, F1337 (2019). A. Kneer, J. Jankovic, D. Susac, A. Putz, N. Wagner, M. Sabharwal and M. Secanell, Journal of The Electrochemical Society, 165, F3241 (2018). Figure