OpenMG: A new multigrid implementation in Python. - Tom S. Bertalan; Akand W. Islam; Roger B. Sidje; Eric S. Carlson (Numer. Linear Algebra Appl., 2014)

Coarse-Grained Descriptions of Dynamics for Networks with Both Intrinsic and Structural Heterogeneities. - Tom Bertalan; Yan Wu; Carlo R. Laing; C. William Gear; Ioannis G. Kevrekidis (Frontiers Comput. Neurosci., 2017)

An Emergent Space for Distributed Data With Hidden Internal Order Through Manifold Learning. - Felix P. Kemeth; Sindre W. Haugland; Felix Dietrich; Tom Bertalan; Kevin Höhlein; Qianxiao Li; Erik M. Bollt; Ronen Talmon; Katharina Krischer; Ioannis G. Kevrekidis (IEEE Access, 2018)

LOCA: LOcal Conformal Autoencoder for standardized data coordinates. - Erez Peterfreund; Ofir Lindenbaum; Felix Dietrich; Tom Bertalan; Matan Gavish; Ioannis G. Kevrekidis; Ronald R. Coifman (CoRR, 2020)

Transformations between deep neural networks. - Tom Bertalan; Felix Dietrich; Ioannis G. Kevrekidis (CoRR, 2020)

Learning emergent PDEs in a learned emergent space. - Felix P. Kemeth; Tom Bertalan; Thomas N. Thiem; Felix Dietrich; Sung Joon Moon; Carlo R. Laing; Ioannis G. Kevrekidis (CoRR, 2020)

Emergent Spaces for Coupled Oscillators. - Thomas N. Thiem; Mahdi Kooshkbaghi; Tom Bertalan; Carlo R. Laing; Ioannis G. Kevrekidis (Frontiers Comput. Neurosci., 2020)

Coarse-grained and Emergent Distributed Parameter Systems from Data. - Hassan Arbabi; Felix P. Kemeth; Tom Bertalan; Ioannis G. Kevrekidis (ACC, 2021)

Initializing LSTM internal states via manifold learning. - Felix P. Kemeth; Tom Bertalan; Nikolaos Evangelou; Tianqi Cui; Saurabh Malani; Ioannis G. Kevrekidis (CoRR, 2021)

Personalized Algorithm Generation: A Case Study in Meta-Learning ODE Integrators. - Yue Guo 006; Felix Dietrich; Tom Bertalan; Danimir T. Doncevic; Manuel Dahmen; Ioannis G. Kevrekidis; Qianxiao Li (CoRR, 2021)

Learning effective stochastic differential equations from microscopic simulations: combining stochastic numerics and deep learning. - Felix Dietrich; Alexei Makeev; George Kevrekidis; Nikolaos Evangelou; Tom Bertalan; Sebastian Reich; Ioannis G. Kevrekidis (CoRR, 2021)

Personalized Algorithm Generation: A Case Study in Learning ODE Integrators. - Yue Guo 006; Felix Dietrich; Tom Bertalan; Danimir T. Doncevic; Manuel Dahmen; Ioannis G. Kevrekidis; Qianxiao Li (SIAM J. Sci. Comput., 2022)

Certified Invertibility in Neural Networks via Mixed-Integer Programming. - Tianqi Cui; Thomas Bertalan; George J. Pappas; Manfred Morari; Yannis G. Kevrekidis; Mahyar Fazlyab (L4DC, 2023)

Data-driven and Physics Informed Modelling of Chinese Hamster Ovary Cell Bioreactors. - Tianqi Cui; Tom S. Bertalan; Nelson Ndahiro; Pratik Khare; Michael Betenbaugh; Costas Maranas; Ioannis G. Kevrekidis (CoRR, 2023)

Some of the variables, some of the parameters, some of the times, with some physics known: Identification with partial information. - Saurabh Malani; Tom S. Bertalan; Tianqi Cui; Jose L. Avalos; Michael Betenbaugh; Ioannis G. Kevrekidis (Comput. Chem. Eng., 2023)

Data-driven and physics informed modeling of Chinese Hamster Ovary cell bioreactors. - Tianqi Cui; Tom Bertalan; Nelson Ndahiro; Pratik Khare; Michael Betenbaugh; Costas Maranas; Ioannis G. Kevrekidis (Comput. Chem. Eng., 2024)

Implementation and (Inverse Modified) Error Analysis for Implicitly Templated ODE-Nets. - Aiqing Zhu; Tom Bertalan; Beibei Zhu; Yifa Tang; Ioannis G. Kevrekidis (SIAM J. Appl. Dyn. Syst., 2024)

Data-Driven, ML-assisted Approaches to Problem Well-Posedness. - Tom Bertalan; George A. Kevrekidis; Eleni D. Koronaki; Siddhartha Mishra; Elizaveta Rebrova; Yannis G. Kevrekidis (CoRR, 2025)