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Agnese Marcato

CNLS Postdoctoral Research Associate
CNLS/EES-16

Machine learning for flow and reactive transport

Agnese Marcato

Office: TA-3, Bldg 1690, Room 136
Mail Stop: B258
Phone: (505) 416-1328
Fax: (505) 665-4055
Email: amarcato@lanl.gov
home page

Research highlight
 Educational Background/Employment:
  • (2019-2023) Ph.D. Chemical Engineering, Politecnico di Torino, Italy
  • (2017-2019) M.Sc. Chemical and Sustainable Processes engineering, Politecnico di Torino, Italy
  • (2014-2017) B.Sc. Chemical and Food Engineering, Politecnico di Torino, Italy
  • Employment:
    • (2024-present) Postdoctoral Resesarch Assiciate, Los Alamos National Laboratory, Los Alamos, NM, USA.

Research Interests:

  • Physics-informed machine learning
  • Differentiable programming
  • Computational Fluid Dynamics
  • Reactive transport in porous media
  • High-performance computing

Selected Recent Publications:

    1. Agnese Marcato, Daniel O'Malley, Hari Viswanathan, Eric Guiltinan, and Javier E. Santos, .Reconstruction of Fields from Sparse Sensing: Differentiable Sensor Placement Enhances Generalization. arXiv preprint arXiv:2312.09176 (2023).
    2. Jaehong Chung, Agnese Marcato, Eric Guiltinan, Tapan Mukerji, Yen Ting Lin, and Javier E. Santos, .Generating Multiphase Fluid Configurations in Fractures using Diffusion Models. arXiv preprint arXiv:2312.04375 (2023).
    3. Javier E. Santos, Agnese Marcato, Qinjun Kang, Mohamed Mehana, Daniel O'Malley, Hari Viswanathan, and Nicholas Lubbers.Learning a General Model of Single Phase Flow in Complex 3D Porous Media. arXiv preprint arXiv:2310.14298 (2023).
    4. Lorenzo Stratta, Merve B. Adali, Antonello Barresi, Gianluca Boccardo, Agnese Marcato, Raffaele Tuccinardi and Roberto Pisano.A diffused-interface model for the lyophilization of a packed bed of spray-frozen particles. Chemical Engineering Science 275 (2023): 118726.
    5. Agnese Marcato, Javier E. Santos, Chaoyue Liu, Gianluca Boccardo, Daniele Marchisio, and Alejandro A. Franco. Modeling the 4D discharge of lithium-ion batteries with a multiscale time-dependent deep learning framework. Energy Storage Materials 63 (2023):102927.
    6. Agnese Marcato, Gianluca Boccardo, and Daniele Marchisio.Reconciling deep learning and first-principle modelling for the investigation of transport phenomena in chemical engineering. The Canadian Journal of Chemical Engineering 101.6 (2023):3013--3018.
    7. Agnese Marcato, Javier E. Santos, Gianluca Boccardo, Hari Viswanathan, Daniele Marchisio, and Ma{\v{s}}a Prodanovi{\'c}. Prediction of local concentration fields in porous media with chemical reaction using a multi scale convolutional neural network. Chemical Engineering Journal 455 (2023): 140367.
    8. Agnese Marcato, Gianluca Boccardo, and Daniele Marchisio.From computational fluid dynamics to structure interpretation via neural networks: an application to flow and transport in porous media. Industrial \& Engineering Chemistry Research 61.24 (2022): 8530--8541.
    9. Agnese Marcato, Gianluca Boccardo, and Daniele Marchisio.A computational workflow to study particle transport and filtration in porous media: Coupling CFD and deep learning. Chemical Engineering Journal 417 (2021): 128936.
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