publications

2026

1. A physics-induced surrogate modeling framework for lithium-ion battery electrode manufacturing

   Utkarsh Vijay, Francisco Fernandez, Diego E. Galvez-Aranda, and 2 more authors

   Journal of Power Sources, Oct 2026

   Abs DOI

   The manufacturing of lithium-ion battery electrodes is a complex, multi-step process. The selection of manufacturing parameters at each stage directly affects the final microstructure of the electrode and, consequently, the electrochemical performance of the cell. Here, we demonstrate the application of our Physics-Induced Electrode (PIE) framework, comprising a set of Machine Learning (ML) models that represent key manufacturing steps: slurry, drying, and calendering. For each step, the ML model uses the coordinates of the particles (representing the active material and carbon-binder aggregates at the mesoscale), along with geometrical descriptors with physical properties, to predict the resulting particle displacement. The model predictions were evaluated using statistical metrics and were found to be significantly accurate. The predicted electrode microstructures were further assessed in terms of their properties and exhibited good agreement with those obtained from purely physics-based simulations. To enhance interpretability, SHapley Additive exPlanations (SHAP) were used to analyze the contribution of the input features to the model predictions. The SHAP analysis showed that the ML predictions capture trends consistent with physical intuition for the manufacturing step analyzed. The PIE framework offers a scalable and interpretable approach that can be integrated into digital solutions to support battery electrode manufacturing pilot lines.

2025

1. A hybrid modelling approach coupling physics-based simulation and deep learning for battery electrode manufacturing simulations

   Utkarsh Vijay, Diego E. Galvez-Aranda, Franco M. Zanotto, and 4 more authors

   Energy Storage Materials, Feb 2025

   Abs DOI

   Lithium-ion battery (LIB) performance is significantly influenced by its manufacturing process. Manufacturing of an optimized electrode can incur high production costs such as high energy consumption, high scrap rates and emissions. This is due to the process that consists of a series of manufacturing steps presenting a complex interrelationship, thus limiting the understanding of performance as a function of manufacturing parameters. While several empirical and computational methods are employed for optimization, they are demanding in terms of resources such as materials or computational effort. By leveraging Deep Learning (DL), we can enhance our understanding of the complex manufacturing processes and accelerate its optimization. We propose a data-driven supervised DL methodology to complement physics-based LIB cathode manufacturing simulations. The trained DL-based predictive model integrates well into a manufacturing simulation framework to forecast cathode slurry microstructures. The DL model demonstrates robust predictive performance for LIB NMC-111 and LiFePO4–based slurries and slurries for a solid-state battery NMC-622/argyrodite composite electrode preparation. While the current work is focused on the electrode slurry preparation process, the proposed methodology has potential for application to drying and calendering steps. This approach will be helpful in streamlining lab-scale electrode manufacturing, and reducing errors, waste and resource consumption.

2. Surrogate Modeling of Lithium-Ion Battery Electrode Manufacturing by Combining Physics-Based Simulation and Deep Learning

   Utkarsh Vijay, Francisco Fernandez, Siwar Ben Hadj Ali, and 2 more authors

   Batteries & Supercaps, 2025

   _eprint: https://chemistry-europe.onlinelibrary.wiley.com/doi/pdf/10.1002/batt.202500433

   Abs DOI

   Optimizing the manufacturing process of Lithium-Ion Batteries (LIB. Finding efficient approaches that accelerate and replace time-consuming, material scrap-expensive trials-and-error optimization methods is a key area of research. This work presents a comprehensive LIB electrode manufacturing framework that combines physics-based simulations with deep learning. This framework efficiently simulates the manufacturing process of LIB electrodes as a function of their formulation. This framework takes the form of a surrogate manufacturing model able to predict the impact of manufacturing parameters on the electrode microstructure and properties. The model is based on a regressor-inspired variational autoencoder method. The analysis of the data and the predicted electrode functional metrics demonstrates the consistency of the approach with an electrode manufacturing model developed on the basis of physics. The reported framework holds significant promise in paving near real time optimization of LIB electrode manufacturing and supporting the optimization of battery cell design in pilot lines.

2023

1. Machine learning for optimal electrode wettability in lithium ion batteries

   Amina El Malki, Mark Asch, Oier Arcelus, and 3 more authors

   Journal of Power Sources Advances, 2023

   DOI Bib PDF

   @article{el_malki_machine_2023,
     title = {Machine learning for optimal electrode wettability in lithium ion batteries},
     volume = {20},
     copyright = {All rights reserved},
     url = {https://www.sciencedirect.com/science/article/pii/S2666248523000069},
     urldate = {2024-01-03},
     doi = {10.1016/j.powera.2023.100114},
     journal = {Journal of Power Sources Advances},
     publisher = {Elsevier},
     author = {El Malki, Amina and Asch, Mark and Arcelus, Oier and Shodiev, Abbos and Yu, Jia and Franco, Alejandro A.},
     year = {2023},
     pages = {100114},
   }

2022

1. 

   A Toolbox for Digital Twins: From Model-Based to Data-Driven

   Mark Asch

   Jan 2022

   DOI Bib

   

   @book{asch_toolbox_2022,
     address = {Philadelphia, PA},
     title = {A {Toolbox} for {Digital} {Twins}: {From} {Model}-{Based} to {Data}-{Driven}},
     isbn = {978-1-61197-696-0 978-1-61197-697-7},
     shorttitle = {A {Toolbox} for {Digital} {Twins}},
     url = {https://epubs.siam.org/doi/book/10.1137/1.9781611976977},
     doi = {10.1137/1.9781611976977},
     language = {en},
     urldate = {2024-01-03},
     publisher = {Society for Industrial and Applied Mathematics},
     author = {Asch, Mark},
     month = jan,
     year = {2022},
   }

2021

1. Cybercosm: New Foundations for a Converged Science Data Ecosystem

   Mark Asch, François Bodin, Micah Beck, and 4 more authors

   Jun 2021

   arXiv:2105.10680 [cs.NI]

   Abs DOI

   Scientific communities naturally tend to organize around data ecosystems created by the combination of their observational devices, their data repositories, and the workflows essential to carry their research from observation to discovery. However, these legacy data ecosystems are now breaking down under the pressure of the exponential growth in the volume and velocity of these workflows, which are further complicated by the need to integrate the highly data intensive methods of the Artificial Intelligence revolution. Enabling ground breaking science that makes full use of this new, data saturated research environment will require distributed systems that support dramatically improved resource sharing, workflow portability and composability, and data ecosystem convergence. The Cybercosm vision presented in this white paper describes a radically different approach to the architecture of distributed systems for data-intensive science and its application workflows. As opposed to traditional models that restrict interoperability by hiving off storage, networking, and computing resources in separate technology silos, Cybercosm defines a minimally sufficient hypervisor as a spanning layer for its data plane that virtualizes and converges the local resources of the system’s nodes in a fully interoperable manner. By building on a common, universal interface into which the problems that infect today’s data-intensive workflows can be decomposed and attacked, Cybercosm aims to support scalable, portable and composable workflows that span and merge the distributed data ecosystems that characterize leading edge research communities today.

2020

1. 3D diarization of a sperm whale click cocktail party by an ultra high sampling rate portable hydrophone array for assessing individual cetacean growth curves

   Maxence Ferrari, Hervé Glotin, Marina Oger, and 4 more authors

   In Forum Acusticum, 2020

   DOI
2. DOCC10: Open access dataset of marine mammal transient studies and end-to-end CNN classification

   Maxence Ferrari, Hervé Glotin, Ricard Marxer, and 1 more author

   In 2020 International Joint Conference on Neural Networks (IJCNN), 2020

   DOI

2019

1. A standardized method of classifying pulsed sounds and its application to pulse rate measurement of blue whale southeast Pacific song units

   Julie Patris, Franck Malige, Hervé Glotin, and 2 more authors

   The Journal of the Acoustical Society of America, 2019

   DOI
2. Mono-hydrophone localization of baleen whales: a study of propagation using a spectral element method applied in Northern Chile

   Julie Patris, Dimitri Komatitsch, Maritza Sepúlveda, and 5 more authors

   In OCEANS 2019-Marseille, 2019

   DOI

2018

1. Big data and extreme-scale computing: Pathways to Convergence-Toward a shaping strategy for a future software and data ecosystem for scientific inquiry

   M Asch, T Moore, R Badia, and 36 more authors

   The International Journal of High Performance Computing Applications, Jul 2018

   Abs DOI

   

   Over the past four years, the Big Data and Exascale Computing (BDEC) project organized a series of five international workshops that aimed to explore the ways in which the new forms of data-centric discovery introduced by the ongoing revolution in high-end data analysis (HDA) might be integrated with the established, simulation-centric paradigm of the high-performance computing (HPC) community. Based on those meetings, we argue that the rapid proliferation of digital data generators, the unprecedented growth in the volume and diversity of the data they generate, and the intense evolution of the methods for analyzing and using that data are radically reshaping the landscape of scientific computing. The most critical problems involve the logistics of wide-area, multistage workflows that will move back and forth across the computing continuum, between the multitude of distributed sensors, instruments and other devices at the networks edge, and the centralized resources of commercial clouds and HPC centers. We suggest that the prospects for the future integration of technological infrastructures and research ecosystems need to be considered at three different levels. First, we discuss the convergence of research applications and workflows that establish a research paradigm that combines both HPC and HDA, where ongoing progress is already motivating efforts at the other two levels. Second, we offer an account of some of the problems involved with creating a converged infrastructure for peripheral environments, that is, a shared infrastructure that can be deployed throughout the network in a scalable manner to meet the highly diverse requirements for processing, communication, and buffering/storage of massive data workflows of many different scientific domains. Third, we focus on some opportunities for software ecosystem convergence in big, logically centralized facilities that execute large-scale simulations and models and/or perform large-scale data analytics. We close by offering some conclusions and recommendations for future investment and policy review.

2016

1. 

   Data Assimilation: Methods, Algorithms, and Applications

   Mark Asch, Marc Bocquet, and Maëlle Nodet

   Dec 2016

   DOI Bib

   

   @book{asch_data_2016,
     address = {Philadelphia, PA},
     title = {Data {Assimilation}: {Methods}, {Algorithms}, and {Applications}},
     isbn = {978-1-61197-453-9 978-1-61197-454-6},
     shorttitle = {Data {Assimilation}},
     url = {http://epubs.siam.org/doi/book/10.1137/1.9781611974546},
     doi = {10.1137/1.9781611974546},
     language = {en},
     urldate = {2024-01-03},
     publisher = {Society for Industrial and Applied Mathematics},
     author = {Asch, Mark and Bocquet, Marc and Nodet, Maëlle},
     month = dec,
     year = {2016},
   }