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20212026
most citedPredicting Energy Budgets in Droplet Dynamics: A Recurrent Neural Network Approach

2 citations · 6 across the 8 of their papers we have counts for

collaborators

9 papers

math.OC2026

Robust Sparse Identification of Nonlinear Dynamics via Least Trimmed Squares

Fabio Amaral, Geovani N. Grapiglia, Cassio M. Oishi

In this work, we propose a robust Sparse Identification of Nonlinear Dynamics (SINDy) pipeline for handling datasets corrupted by noise and outliers. The method decouples outlier f…

physics.flu-dyn2026

Prediction of Viscoelastic Droplet Impact Dynamics Using a Vision Transformer-Based Approach

Diego A. de Aguiar, Cassio M. Oishi

Droplet impact on solid surfaces is a complex fluid dynamics problem with applications in spray cooling, inkjet printing, and pharmaceutical processing. Although numerical simulati…

stat.ML2025

From STLS to Projection-based Dictionary Selection in Sparse Regression for System Identification

Hangjun Cho, Fabio V. G. Amaral, Andrei A. Klishin +2

In this work, we revisit dictionary-based sparse regression, in particular, Sequential Threshold Least Squares (STLS), and propose a score-guided library selection to provide pract…

cs.LG2025

CS-SHRED: Enhancing SHRED for Robust Recovery of Spatiotemporal Dynamics

Romulo B. da Silva, Diego Passos, Cássio M. Oishi +1

We present CS-SHRED, a novel deep learning architecture that integrates Compressed Sensing (CS) into a Shallow Recurrent Decoder (SHRED) to reconstruct spatiotemporal dynamics from…

physics.flu-dyn2024★ 2 cited

Machine Learning in Viscoelastic Fluids via Energy-Based Kernel Embedding

Samuel E. Otto, Cassio M. Oishi, Fabio Amaral +2

The ability to measure differences in collected data is of fundamental importance for quantitative science and machine learning, motivating the establishment of metrics grounded in…

physics.flu-dyn2024★ 2 cited

Predicting Energy Budgets in Droplet Dynamics: A Recurrent Neural Network Approach

Diego A. de Aguiar, Hugo L. França, Cassio M. Oishi

Neural networks in fluid mechanics offer an efficient approach for exploring complex flows, including multiphase and free surface flows. The recurrent neural network, particularly…

physics.flu-dyn2023★ 1 cited

Nonlinear parametric models of viscoelastic fluid flows

Cassio M. Oishi, Alan A. Kaptanoglu, J. Nathan Kutz +1

Reduced-order models have been widely adopted in fluid mechanics, particularly in the context of Newtonian fluid flows. These models offer the ability to predict complex dynamics,…

cond-mat.soft2023

Elasto-viscoplastic Spreading: from Plastocapillarity to Elastocapillarity

Hugo L. Franc, Maziyar Jalaal, Cassio M. Oishi

We study the spreading of elastoviscoplastic (EVP) droplets under surface tension effects. The non- Newtonian material flows like a viscoelastic liquid above the yield stress and b…

math.NA2021★ 1 cited

Second-order finite difference approximations of the upper-convected time derivative

Debora O. Medeiros, Hirofumi Notsu, Cassio M. Oishi

In this work, new finite difference schemes are presented for dealing with the upper-convected time derivative in the context of the generalized Lie derivative. The upper-convected…