output
20202026
most citedAssessment of unsteady flow predictions using hybrid deep learning based reduced order models

106 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

4 papers

cs.CE2026★ 1 cited

PINEAPPLE: Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter Inference in Lithium-Ion Battery Electrodes

Karkulali Pugalenthi, Jian Cheng Wong, Qizheng Yang +4

Accurate, real-time, yet non-destructive estimation of internal states in lithium-ion batteries is critical for predicting degradation, optimizing usage strategies, and extending o…

cs.NE2023★ 1 cited

Evolutionary Optimization of Physics-Informed Neural Networks: Advancing Generalizability by the Baldwin Effect

Jian Cheng Wong, Chin Chun Ooi, Abhishek Gupta +4

Physics-informed neural networks (PINNs) are at the forefront of scientific machine learning, making possible the creation of machine intelligence that is cognizant of physical law…

physics.ao-ph2022★ 12 cited

Variability of wave power production of the M4 machine at two energetic open ocean locations: off Albany, Western Australia and at EMEC, Orkney, UK

Jana Orszaghova, Siane Lemoine, Harrif Santo +5

Since intermittent and highly variable power supply is undesirable, quantifying power yield fluctuations of wave energy converters (WECs) aids with assessment of potential deployme…

physics.flu-dyn2020★ 106 cited

Assessment of unsteady flow predictions using hybrid deep learning based reduced order models

Sandeep Reddy Bukka, Rachit Gupta, Allan Ross Magee +1

In this paper, we present two deep learning-based hybrid data-driven reduced order models for the prediction of unsteady fluid flows. The first model projects the high-fidelity tim…