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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Maple pecan pie
Published:
This is a maple pecan pie without corn syrup, less sweet than usual.
Elise Drømmekage: Coconut vanilla cake
Published:
Ingredients
Banana Chocolate Chip Bread
Published:
Ingredients (for 6 servings)
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
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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talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.