A fast numerical scheme for causal relativistic hydrodynamics with dissipation. - Makoto Takamoto; Shu-ichiro Inutsuka (J. Comput. Phys., 2011)

A new scheme of causal viscous hydrodynamics for relativistic heavy-ion collisions: A Riemann solver for quark-gluon plasma. - Yukinao Akamatsu; Shu-ichiro Inutsuka; Chiho Nonaka; Makoto Takamoto (J. Comput. Phys., 2014)

An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation. - Makoto Takamoto; Yusuke Morishita; Hitoshi Imaoka (MIPR, 2020)

An Empirical Study of the Effects of Sample-Mixing Methods for Efficient Training of Generative Adversarial Networks. - Makoto Takamoto; Yusuke Morishita (MIPR, 2021)

Integrating diverse extraction pathways using iterative predictions for Multilingual Open Information Extraction. - Bhushan Kotnis; Kiril Gashteovski; Carolin Lawrence; Daniel Oñoro-Rubio; Vanesa Rodriguez-Tembras; Makoto Takamoto; Mathias Niepert (CoRR, 2021)

MILIE: Modular & Iterative Multilingual Open Information Extraction. - Bhushan Kotnis; Kiril Gashteovski; Daniel Oñoro-Rubio; Ammar Shaker; Vanesa Rodriguez-Tembras; Makoto Takamoto; Mathias Niepert; Carolin Lawrence (ACL (1), 2022)

PDEBench: An Extensive Benchmark for Scientific Machine Learning. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (NeurIPS, 2022)

PDEBench Pretrained Models. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2022)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2022)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2022)

Learning Neural PDE Solvers with Parameter-Guided Channel Attention. - Makoto Takamoto; Francesco Alesiani; Mathias Niepert (ICML, 2023)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

PDEBench Pretrained Models. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing. - Viktor Zaverkin; Francesco Alesiani; Takashi Maruyama; Federico Errica; Henrik Christiansen; Makoto Takamoto; Nicolas Weber; Mathias Niepert (NeurIPS, 2024)

Physics-Informed Weakly Supervised Learning for Interatomic Potentials. - Makoto Takamoto; Viktor Zaverkin; Mathias Niepert (CoRR, 2024)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2024)

Research Data Management in Simulation Science: Infrastructure, Tools, and Applications. - Bernd Flemisch; Sibylle Hermann; Melanie Herschel; Dirk Pflüger; Jürgen Pleiss; Jan Range; Sarbani Roy; Makoto Takamoto; Benjamin Uekermann (Datenbank-Spektrum, 2024)

Active Learning for Neural PDE Solvers. - Daniel Musekamp; Marimuthu Kalimuthu; David Holzmüller; Makoto Takamoto; Mathias Niepert (ICLR, 2025)

Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction. - Makoto Takamoto; Daniel Oñoro-Rubio; Wiem Ben Rim; Takashi Maruyama; Bhushan Kotnis (Trans. Mach. Learn. Res., 2025)

Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations. - Henrik Christiansen; Takashi Maruyama; Federico Errica; Viktor Zaverkin; Makoto Takamoto; Francesco Alesiani (CoRR, 2025)