| A computational workflow to study particle transport and filtration in porous media: Coupling CFD and deep learning A Marcato, G Boccardo, D Marchisio Chemical Engineering Journal 417, 128936, 2021 | 68 | 2021 |
| From computational fluid dynamics to structure interpretation via neural networks: an application to flow and transport in porous media A Marcato, G Boccardo, D Marchisio Industrial & Engineering Chemistry Research 61 (24), 8530-8541, 2022 | 37 | 2022 |
| Prediction of local concentration fields in porous media with chemical reaction using a multi scale convolutional neural network A Marcato, JE Santos, G Boccardo, H Viswanathan, D Marchisio, ... Chemical Engineering Journal 455, 140367, 2023 | 22 | 2023 |
| Reconciling deep learning and first‐principle modelling for the investigation of transport phenomena in chemical engineering A Marcato, D Marchisio, G Boccardo The Canadian Journal of Chemical Engineering 101 (6), 3013-3018, 2023 | 10 | 2023 |
| Modeling the 4D discharge of lithium-ion batteries with a multiscale time-dependent deep learning framework A Marcato, JE Santos, C Liu, G Boccardo, D Marchisio, AA Franco Energy Storage Materials 63, 102927, 2023 | 8 | 2023 |
| A computational workflow to study particle transport in porous media: coupling CFD and deep learning A Marcato, G Boccardo, DL Marchisio Computer Aided Chemical Engineering 48, 1759-1764, 2020 | 7 | 2020 |
| A diffused-interface model for the lyophilization of a packed bed of spray-frozen particles L Stratta, MB Adali, AA Barresi, G Boccardo, A Marcato, R Tuccinardi, ... Chemical Engineering Science 275, 118726, 2023 | 3 | 2023 |
| Journey over destination: dynamic sensor placement enhances generalization A Marcato, E Guiltinan, H Viswanathan, D O’Malley, N Lubbers, JE Santos Machine Learning: Science and Technology 5 (2), 025070, 2024 | 1 | 2024 |
| A transformer-based model for grid-agnostic full-field reconstruction of tsunami waves from sparse observations. E McDugald, A Mohan, D Engwirda, J Santos, A Marcato Bulletin of the American Physical Society, 2024 | | 2024 |
| Upscaling of Lithium-Ion Battery Models: From the Pore-Scale to the Cell-Scale through Homogenization. AL Pontillo, A Marcato, A Buffo, G Boccardo, D Marchisio, I Battiato 2024 AIChE Annual Meeting, 2024 | | 2024 |
| Accelerating Multiphase Flow Simulations with Denoising Diffusion Model Driven Initializations J Chung, A Marcato, EJ Guiltinan, T Mukerji, H Viswanathan, YT Lin, ... arXiv preprint arXiv:2406.19333, 2024 | | 2024 |
| Learning a general model of single phase flow in complex 3D porous media JE Santos, A Marcato, Q Kang, M Mehana, D O’Malley, H Viswanathan, ... Machine Learning: Science and Technology 5 (2), 025039, 2024 | | 2024 |
| Reconstruction of Fields from Sparse Sensing: Differentiable Sensor Placement Enhances Generalization A Marcato, D O'Malley, H Viswanathan, E Guiltinan, JE Santos arXiv preprint arXiv:2312.09176, 2023 | | 2023 |
| Generating Multiphase Fluid Configurations in Fractures using Diffusion Models J Chung, A Marcato, EJ Guiltinan, T Mukerji, YT Lin, JE Santos arXiv preprint arXiv:2312.04375, 2023 | | 2023 |
| Deep neural networks as scale-bridging tools for flow and transport modelling in porous media G Boccardo, A Marcato, JE Santos, M Prodanovic, D Marchisio AGU Fall Meeting Abstracts 2023, H41E-07, 2023 | | 2023 |
| Improving Sensor Placement for Enhanced Field Reconstruction: A Machine Learning Differentiable Workflow A Marcato, EJ Guiltinan, D O'Malley, HS Viswanathan, JE Santos AGU Fall Meeting Abstracts 2023, H21C-07, 2023 | | 2023 |
| Generating Multiphase Flow Configurations Using Diffusion Models J Chung, A Marcato, E Guiltinan, YT Lin, JE Santos AGU Fall Meeting Abstracts 2023, H31I-08, 2023 | | 2023 |
| PySimFrac: A Python Library for Generating, Analyzing, and Simulating Synthetic Fractures EJ Guiltinan, JE Santos, P Purswani, A Marcato, J Hyman AGU Fall Meeting Abstracts 2023 (1588), H13L-1588, 2023 | | 2023 |
| Deep neural networks as data-driven models for flow and transport in porous media A Marcato Politecnico di Torino, 2023 | | 2023 |
| Machine learning for multivariate parameter identification of first-principle model: the Mg (OH) 2 test case A Raponi, A Marcato, G Boccardo, A Buffo, M Vanni, D Marchisio -, 2023 | | 2023 |
