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Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning

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npj computational Materials 2023年第1期9卷 1402-1410页

作者： Ying Huang David Perlmutter Andrea Fei-Huei Su Jerome Quenum Pavel Shevchenko Dilworth Y.Parkinson Iryna V.Zenyuk Daniela Ushizima Department of Materials Science&Engineering University of California IrvineIrvineCaliforniaUSA National Fuel Cell Research Center University of California IrvineIrvineCaliforniaUSA Applied Math and Computational Research Division Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Department of Chemical&Biomolecular Engineering University of California IrvineIrvineCaliforniaUSA Department of Electrical Engineering and Computer Sciences College of EngineeringUniversity of California BerkeleyBerkeleyCaliforniaUSA Advanced Photon Source Argonne National Laboratory9700 South Cass AvenueLemontIllinoisUSA Advanced Light Source Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Berkeley Institute for Data Science University of California BerkeleyBerkeleyCaliforniaUSA

Operando X-ray micro-computed tomography(µCT)provides an opportunity to observe the evolution of Li structures inside pouch *** is an essential step to quantitatively analyzingµCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the ***,we report a computational approach,batteryNET,to train an Iterative Residual U-Net-based network to detect Li *** resulting semantic segmentation shows singular Li-related component changes,addressing diverse morphologies in the *** addition,visualizations of the dead Li are provided,including calculations about the volume and effective thickness of electrodes,deposited Li,and redeposited *** also report discoveries about the spatial relationships between these *** approach focuses on a method for analyzing battery performance,which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.

关键词： battery lithium state

LEAST H2 NORM UPDATING QUADRATIC INTERPOLATION MODEL FUNCTION FOR DERIVATIVE-FREE TRUST-REGION ALGORITHMS

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arXiv 2023年

作者： Xie, Pengcheng Yuan, Ya-Xiang Institute of Computational Math and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences China

Derivative-free optimization methods are numerical methods for optimization problems in which no derivative information is used. Such optimization problems are widely seen in many real applications. One particular class of derivative-free optimization algorithms is trust-region algorithms based on quadratic models given by underdetermined interpolation. Different techniques in updating the quadratic model from iteration to iteration will give different interpolation models. In this paper, we propose a new way to update the quadratic model by minimizing the H2 norm of the difference between neighbouring quadratic models. Motivation for applying the H2 norm is given, and theoretical properties of our new updating technique are also presented. Projection in the sense of H2 norm and interpolation error analysis of our model function are proposed. We obtain the coefficients of the quadratic model function by using the KKT conditions. Numerical results show advantages of our model, and the derivative-free algorithms based on our least H2 norm updating quadratic model functions can solve test problems with fewer function evaluations than algorithms based on least Frobenius norm *** Codes 90C56, 90C30, 65K05, 90C90 Copyright © 2023, The Authors. All rights reserved.

关键词： Interpolation

Safe Drone Flight with Time-Varying Backup Controllers

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arXiv 2022年

作者： Singletary, Andrew Swann, Aiden Rodriguez, Ivan Dario Jimenez Ames, Aaron D. Department of Mechanical and Civil Engineering Deparment of Computational Math and Science California Institute of Technology PasadenaCA91125 United States

The weight, space, and power limitations of small aerial vehicles often prevent the application of modern control techniques without significant model simplifications. Moreover, high-speed agile behavior, such as that exhibited in drone racing, make these simplified models too unreliable for safety-critical control. In this work, we introduce the concept of time-varying backup controllers (TBCs): user-specified maneuvers combined with backup controllers that generate reference trajectories which guarantee the safety of nonlinear systems. TBCs reduce conservatism when compared to traditional backup controllers and can be directly applied to multi-agent coordination to guarantee safety. Theoretically, we provide conditions under which TBCs strictly reduce conservatism, describe how to switch between several TBC's and show how to embed TBCs in a multi-agent setting. Experimentally, we verify that TBCs safely increase operational freedom when filtering a pilot's actions and demonstrate robustness and computational efficiency when applied to decentralized safety filtering of two quadrotors. Copyright © 2022, The Authors. All rights reserved.

关键词： Controllers

Addressing Racial Bias in Facial Emotion Recognition

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arXiv 2023年

作者： Fan, Alex Xiao, Xingshuo Washington, Peter Department of Statistics Stanford University StanfordCA United States Institute of Computational Math and Engineering Stanford University StanfordCA United States Information and Computer Science University of Hawai’i at Manoa HonoluluHI United States

Fairness in deep learning models trained with high-dimensional inputs and subjective labels remains a complex and understudied area. Facial emotion recognition, a domain where datasets are often racially imbalanced, can lead to models that yield disparate outcomes across racial groups. This study focuses on analyzing racial bias by sub-sampling training sets with varied racial distributions and assessing test performance across these simulations. Our findings indicate that smaller datasets with posed faces improve on both fairness and performance metrics as the simulations approach racial balance. Notably, the F1-score increases by 27.2% points, and demographic parity increases by 15.7% points on average across the simulations. However, in larger datasets with greater facial variation, fairness metrics generally remain constant, suggesting that racial balance by itself is insufficient to achieve parity in test performance across different racial groups. © 2023, CC BY.

关键词： Emotion Recognition

Deep neural operators can predict the real-time response of floating offshore structures under irregular waves

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arXiv 2023年

作者： Cao, Qianying Goswami, Somdatta Tripura, Tapas Chakraborty, Souvik Karniadakis, George Em State Key Lab of Coastal and Offshore Engineering Dalian University of Technology China Division of Applied Mathematics Brown University United States School of Engineering Brown University United States Computational Math Group Pacific Northwest National Laboratory United States Department of Applied Mechanics Indian Institute of Technology Delhi India School of Artificial Intelligence Indian Institute of Technology Delhi India

The utilization of neural operators in a digital twin model of an offshore floating structure holds the potential for a significant shift in the prediction of structural responses and health monitoring, offering valuable real-time control insights. In this work, we investigate the effectiveness of three neural operators, namely the deep operator network (DeepONet), the Fourier neural operator (FNO), and the Wavelet neural operator (WNO), to accurately capture the responses of a floating structure under six different sea state codes (3 − 8) based on the wave characteristics described by the World Meteorological Organization (WMO). To further enhance the accuracy of the vanilla architecture of the neural operators, novel extensions, such as wavelet-DeepONet and self-adaptive WNO, are proposed in this paper. The results demonstrate that these high-precision neural operators can deliver structural responses more efficiently, up to two orders of magnitude faster than a dynamic analysis using conventional numerical solvers. Additionally, compared to gated recurrent units (GRUs), a commonly used recurrent neural network for time-series estimation, neural operators are both more accurate and efficient, especially in situations with limited data availability. Taken together, our study shows that FNO outperforms all other operators for approximating the mapping of one input functional space to the output space as well as for responses that have small bandwidth of the frequency spectrum. Conversely, DeepONet, with historical states, proves most accurate in learning the mapping of multiple input functions to the output space and capturing responses within a broad frequency spectrum. © 2023, CC BY-NC-SA.

关键词： Mapping

Graph-Based Active Learning for Surface Water and Sediment Detection in Multispectral Images

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Graph-Based Active Learning for Surface Water and Sediment D...

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Bohan Chen Kevin Miller Andrea L. Bertozzi Jon Schwenk Los Angeles. Math Dept. University of California Los Angeles CA USA Austin. Oden Institute for Computational Engineering and Sciences University of Texas Austin TX USA Earth & Env. Sci. Division Los Alamos National Laboratory NM USA

We develop a graph active learning pipeline (GAP) to detect surface water and in-river sediment pixels in satellite images. The active learning approach is applied within the training process to optimally select specific pixels to generate a hand-labeled training set. Our method obtains higher accuracy with far fewer training pixels than both standard and deep learning models. According to our experiments, our GAP trained on a set of 3270 pixels reaches a better accuracy than the neural network method trained on 2.1 million pixels.

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Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems:A Review

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1361-1387页

作者： Sibo Cheng César Quilodrán-Casas Said Ouala Alban Farchi Che Liu Pierre Tandeo Ronan Fablet Didier Lucor Bertrand Iooss Julien Brajard Dunhui Xiao Tijana Janjic Weiping Ding Yike Guo Alberto Carrassi Marc Bocquet Rossella Arcucci Data Science Institute Department of ComputingImperial College LondonSW72AZ London Department of Earth Science and Engineering Imperial College LondonSW72AZ London Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China the IMT Atlantique Lab-STICCUMR CNRS 6285France and OdysseyInria/IMTFrance.P.Tandeo is also with RIKEN Center for Computational ScienceKobeJapan the CEREA École des Ponts and EDF R&Dîle-de-FranceFrance the Laboratoire Interdisciplinaire des Sciences du Numérique CNRSParis-Saclay UniversityF-91403OrsayFrance the Electricitéde France(EDF) 78401 ChatouFranceInstitut de Mathématiques de Toulouse31062 ToulouseFrance and SINCLAIR AI LabSaclayFrance the Sorbonne University ParisFranceand also with Nansen Environmental and Remote Sensing Center(NERSC)BergenNorway the School of Mathematical Sciences Tongji UniversityShanghai 200092China the Mathematical Institute for Machine Learning and Data Science KU Eichstaett-IngolstadtBavariaGermany the School of Information Science and Technology Nantong UniversityNantong 226019China the Department of Physics and Astronomy“Augusto Righi” University of Bologna40124 BolognaItaly

Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal *** applications span from computational fluid dynamics(CFD)to geoscience and climate ***,much effort has been given in combining DA,UQ and machine learning(ML)*** research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive *** paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of *** review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their ***,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice *** exciting perspectives of this rapidly developing research field are also *** Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

关键词： assimilation overcome apply

Enhancing Biomechanical Simulations Based on A Posteriori Error Estimates: The Potential of Dual Weighted Residual-Driven Adaptive Mesh Refinement

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arXiv 2024年

作者： Bui, Huu Phuoc Duprez, Michel Rohan, Pierre-Yves Lejeune, Arnaud Bordas, Stéphane P.A. Bucki, Marek Chouly, Franz Ansys Lyon France MIMESIS MLMS Inria Nancy Grand-Est Université de Strasbourg Strasbourg France Institut de Biomécanique Humaine Georges Charpak Arts et Métiers Institute of technology Paris France Department of Applied Mechanics FEMTO-ST Institute University of Franche-Comte UMR 6174 CNRS Besançon France Institute of Computational Engineering Department of Engineering Luxembourg TwInsight Grenoble France Institut de Mathématiques de Bourgogne Université de Bourgogne Dijon France Mathematical Modeling and Department of Mathematical Engineering University of Chile Santiago Chile Departamento de Ingeniería Matemática CI2MA Universidad de Concepción Concepción Chile

The Finite Element Method (FEM) is a well-established procedure for computing approximate solutions to deterministic engineering problems described by partial differential equations. FEM produces discrete approximations of the solution with a discretisation error that can be an be quantified with a posteriori error estimates. The practical relevance of error estimates for biomechanics problems, especially for soft tissue where the response is governed by large strains, is rarely addressed. In this contribution, we propose an implementation of a posteriori error estimates targeting a user-defined quantity of interest, using the Dual Weighted Residual (DWR) technique tailored to biomechanics. The proposed method considers a general setting that encompasses three-dimensional geometries and model non-linearities, which appear in hyperelastic soft tissues. We take advantage of the automatic differentiation capabilities embedded in modern finite element software, which allows the error estimates to be computed generically for a large class of models and constitutive laws. First we validate our methodology using experimental measurements from silicone samples, and then illustrate its applicability for patient-specific computations of pressure ulcers on a human *** Codes 65N30, 92C10, 74B20 © 2024, CC BY.

关键词： Biomechanics

ExEnDiff: An Experiment-Guided Diffusion Model for Protein Conformational Ensemble Generation

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PRX Life

PRX Life 2025年第2期3卷 023013-023013页

作者： Yikai Liu Zongxin Yu Richard J. Lindsay Guang Lin Ming Chen Abhilash Sahoo Sonya M. Hanson Department of Mechanical Engineering Purdue University West Lafayette Indiana 47907 USA Center for Computational Biology Flatiron Institute New York New York 10010 USA Department of Engineering Sciences and Applied Math Northwestern University Evanston Illinois 60201 USA Center for Computational Mathematics Flatiron Institute New York New York 10010 USA Department of Chemistry Purdue University West Lafayette Indiana 47907 USA

Understanding protein conformation is key to understanding their function. Importantly, most proteins adopt multiple conformations with nontrivial ensemble distributions that change depending on their environment to perform functions like catalysis, signaling, and transport. Recently, machine learning techniques, especially deep generative models, have been employed to develop protein conformation generators. These models, known as unified protein ensemble samplers, are trained on the Protein Data Bank (PDB) dataset and can generate diverse protein conformation ensembles given a protein sequence. However, their reliance solely on structural data from the PDB, which primarily captures folded protein states, restricts the diversity of the generated ensembles and can result in physically unrealistic conformations. In this paper, we overcome these challenges by introducing ExEnDiff, an experiment-guided diffusion model for protein conformation generation. ExEnDiff integrates experimental measurements as a physical prior, enabling the generation of protein conformations with desired properties. Our experiments on a variety of fast-folding and intrinsically disordered proteins demonstrate that ExEnDiff significantly advances the capabilities of current unified protein ensemble samplers. With little computational cost, ExEnDiff can capture important proteins' configuration properties and the underlying Boltzmann distribution, paving the way for a next-generation molecular dynamics engine. We further demonstrate the effectiveness of ExEnDiff to capture conformational changes in the presence of mutations and as an efficient tool for determining a reasonable collective variable space for protein ensembles. With these results, ExEnDiff is well poised to push the study of protein ensembles into a data-rich regime currently available to few problems in biology.

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