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Physics-informed long short-term memory for forecasting and reconstruction of chaos

International Conference on Computational Science 2023, 2023

This paper introduces the physics-informed LSTM (PI-LSTM) to reconstruct and forecast unmeasured variables in chaotic systems, constrained by governing equations to improve stability and predictive accuracy.

Citation: Özalp, E., Margazoglou, G., & Magri, L. (2023). Physics-informed long short-term memory for forecasting and reconstruction of chaos. In International Conference on Computational Science (Vol. 10476, pp. 382–389). Springer, Cham.
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Trends in Explainable Artificial Intelligence for Non-Experts

KI-Kritik/AI Critique Volume 4, 2023

This article from my M.Sc. examines the developments in eXplainable Artificial Intelligence (XAI) aimed at improving interpretability for non-expert users, with a focus on biases related to gender, race, and socioeconomic status.

Citation: Özalp, E., Hartwig, K., & Reuter, C. (2023). Trends in Explainable Artificial Intelligence for Non-Experts. KI-Kritik/AI Critique Volume 4, 223.
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Reconstruction, forecasting, and stability of chaotic dynamics from partial data

Chaos: An Interdisciplinary Journal of Nonlinear Science, 2023

This paper explores LSTMs for reconstructing and forecasting from partial chaotic observations, and analyses their stability properties.

Citation: Özalp, E., Margazoglou, G., & Magri, L. (2023). Reconstruction, forecasting, and stability of chaotic dynamics from partial data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 33(9).
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Stability analysis of chaotic systems in latent spaces

arXiv preprint arXiv:2410.00480, 2024

This study demonstrates a latent-space approach using a CAE-ESN model to analyze the stability properties of chaotic partial differential equations, with a focus on the Kuramoto-Sivashinsky equation.

Citation: Özalp, E., & Magri, L. (2024). Stability analysis of chaotic systems in latent spaces. In Review. arXiv preprint arXiv:2410.00480.
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Inferring stability properties of chaotic systems on autoencoders’ latent spaces

Machine Learning and the Physical Sciences Workshop, NeurIPS, 2024

This work demonstrates the CAE-ESN model’s ability to infer stability properties of chaotic systems in a low-dimensional latent space, using Lyapunov exponents and covariant Lyapunov vectors to represent the geometry of the tangent space.

Citation: Özalp, E., & Magri, L. (2024). Inferring stability properties of chaotic systems on autoencoders’ latent spaces. Machine Learning and the Physical Sciences Workshop, NeurIPS 2024.
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Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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