RUDDER: Return Decomposition for Delayed Rewards. - Jose A. Arjona-Medina; Michael Gillhofer; Michael Widrich; Thomas Unterthiner; Johannes Brandstetter; Sepp Hochreiter (NeurIPS, 2019)

Quantum Optical Experiments Modeled by Long Short-Term Memory. - Thomas Adler; Manuel Erhard; Mario Krenn; Johannes Brandstetter; Johannes Kofler; Sepp Hochreiter (CoRR, 2019)

Using LSTMs for climate change assessment studies on droughts and floods. - Frederik Kratzert; Daniel Klotz; Johannes Brandstetter; Pieter-Jan Hoedt; Grey Nearing; Sepp Hochreiter (CoRR, 2019)

XAI and Strategy Extraction via Reward Redistribution. - Marius-Constantin Dinu; Markus Hofmarcher; Vihang Prakash Patil; Matthias Dorfer; Patrick M. Blies; Johannes Brandstetter; Jose A. Arjona-Medina; Sepp Hochreiter (xxAI@ICML, 2020)

Modern Hopfield Networks and Attention for Immune Repertoire Classification. - Michael Widrich; Bernhard Schäfl; Milena Pavlovic; Hubert Ramsauer; Lukas Gruber; Markus Holzleitner; Johannes Brandstetter; Geir Kjetil Sandve; Victor Greiff; Sepp Hochreiter; Günter Klambauer (NeurIPS, 2020)

Hopfield Networks is All You Need. - Hubert Ramsauer; Bernhard Schäfl; Johannes Lehner; Philipp Seidl; Michael Widrich; Lukas Gruber; Markus Holzleitner; Milena Pavlovic; Geir Kjetil Sandve; Victor Greiff; David P. Kreil; Michael Kopp 001; et al. (CoRR, 2020)

Cross-Domain Few-Shot Learning by Representation Fusion. - Thomas Adler; Johannes Brandstetter; Michael Widrich; Andreas Mayr; David P. Kreil; Michael Kopp 001; Günter Klambauer; Sepp Hochreiter (CoRR, 2020)

Learning Lagrangian Fluid Mechanics with E(3)-Equivariant Graph Neural Networks. - Artur P. Toshev; Gianluca Galletti; Johannes Brandstetter; Stefan Adami; Nikolaus A. Adams (GSI (2), 2021)

Hopfield Networks is All You Need. - Hubert Ramsauer; Bernhard Schäfl; Johannes Lehner; Philipp Seidl; Michael Widrich; Lukas Gruber; Markus Holzleitner; Thomas Adler; David P. Kreil; Michael K. Kopp 001; Günter Klambauer; Johannes Brandstetter; et al. (ICLR, 2021)

Learning 3D Granular Flow Simulations. - Andreas Mayr; Sebastian Lehner; Arno Mayrhofer; Christoph Kloss; Sepp Hochreiter; Johannes Brandstetter (CoRR, 2021)

Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER. - Markus Holzleitner; Lukas Gruber; José Antonio Arjona-Medina; Johannes Brandstetter; Sepp Hochreiter (Trans. Large Scale Data Knowl. Centered Syst., 2021)

Few-Shot Learning by Dimensionality Reduction in Gradient Space. - Martin Gauch; Maximilian Beck; Thomas Adler; Dmytro Kotsur; Stefan Fiel; Hamid Eghbal-zadeh; Johannes Brandstetter; Johannes Kofler; Markus Holzleitner; Werner Zellinger; Daniel Klotz; Sepp Hochreiter; et al. (CoLLAs, 2022)

Geometric and Physical Quantities improve E(3) Equivariant Message Passing. - Johannes Brandstetter; Rob Hesselink; Elise van der Pol; Erik J. Bekkers; Max Welling (ICLR, 2022)

Message Passing Neural PDE Solvers. - Johannes Brandstetter; Daniel E. Worrall; Max Welling (ICLR, 2022)

Lie Point Symmetry Data Augmentation for Neural PDE Solvers. - Johannes Brandstetter; Max Welling; Daniel E. Worrall (ICML, 2022)

Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution. - Vihang Patil; Markus Hofmarcher; Marius-Constantin Dinu; Matthias Dorfer; Patrick M. Blies; Johannes Brandstetter; José Antonio Arjona-Medina; Sepp Hochreiter (ICML, 2022)

Boundary Graph Neural Networks for 3D Simulations. - Andreas Mayr; Sebastian Lehner; Arno Mayrhofer; Christoph Kloss; Sepp Hochreiter; Johannes Brandstetter (AAAI, 2023)

Clifford Neural Layers for PDE Modeling. - Johannes Brandstetter; Rianne van den Berg; Max Welling; Jayesh K. Gupta (ICLR, 2023)

ClimaX: A foundation model for weather and climate. - Tung Nguyen; Johannes Brandstetter; Ashish Kapoor; Jayesh K. Gupta; Aditya Grover (ICML, 2023)

Geometric Clifford Algebra Networks. - David Ruhe; Jayesh K. Gupta; Steven De Keninck; Max Welling; Johannes Brandstetter (ICML, 2023)

Lie Point Symmetry and Physics-Informed Networks. - Tara Akhound-Sadegh; Laurence Perreault Levasseur; Johannes Brandstetter; Max Welling; Siamak Ravanbakhsh (NeurIPS, 2023)

PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers. - Phillip Lippe; Bas Veeling; Paris Perdikaris; Richard E. Turner; Johannes Brandstetter (NeurIPS, 2023)

Clifford Group Equivariant Neural Networks. - David Ruhe; Johannes Brandstetter; Patrick Forré (NeurIPS, 2023)

G-Signatures: Global Graph Propagation With Randomized Signatures. - Bernhard Schäfl; Lukas Gruber; Johannes Brandstetter; Sepp Hochreiter (CoRR, 2023)

E(3) Equivariant Graph Neural Networks for Particle-Based Fluid Mechanics. - Artur P. Toshev; Gianluca Galletti; Johannes Brandstetter; Stefan Adami; Nikolaus A. Adams (CoRR, 2023)

Towards Multi-spatiotemporal-scale Generalized PDE Modeling. - Jayesh K. Gupta; Johannes Brandstetter (Trans. Mach. Learn. Res., 2023)

GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks. - Lisa Schneckenreiter; Richard Freinschlag; Florian Sestak; Johannes Brandstetter; Günter Klambauer; Andreas Mayr (Tiny Papers @ ICLR, 2024)

Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics. - Artur P. Toshev; Jonas A. Erbesdobler; Nikolaus A. Adams; Johannes Brandstetter (ICML, 2024)

Clifford-Steerable Convolutional Neural Networks. - Maksim Zhdanov 001; David Ruhe; Maurice Weiler; Ana Lucic; Johannes Brandstetter; Patrick Forré (ICML, 2024)

Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators. - Benedikt Alkin; Andreas Fürst; Simon Schmid; Lukas Gruber; Markus Holzleitner; Johannes Brandstetter (NeurIPS, 2024)

xLSTM: Extended Long Short-Term Memory. - Maximilian Beck; Korbinian Pöppel; Markus Spanring; Andreas Auer; Oleksandra Prudnikova; Michael Kopp 001; Günter Klambauer; Johannes Brandstetter; Sepp Hochreiter (NeurIPS, 2024)

MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations. - Benedikt Alkin; Lukas Miklautz; Sepp Hochreiter; Johannes Brandstetter (CoRR, 2024)

Universal Physics Transformers. - Benedikt Alkin; Andreas Fürst; Simon Schmid; Lukas Gruber; Markus Holzleitner; Johannes Brandstetter (CoRR, 2024)

Geometry-Informed Neural Networks. - Arturs Berzins; Andreas Radler; Sebastian Sanokowski; Sepp Hochreiter; Johannes Brandstetter (CoRR, 2024)

JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework. - Artur P. Toshev; Harish Ramachandran; Jonas A. Erbesdobler; Gianluca Galletti; Johannes Brandstetter; Nikolaus A. Adams (CoRR, 2024)

VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification. - Florian Sestak; Lisa Schneckenreiter; Johannes Brandstetter; Sepp Hochreiter; Andreas Mayr; Günter Klambauer (CoRR, 2024)

Aurora: A Foundation Model of the Atmosphere. - Cristian Bodnar; Wessel P. Bruinsma; Ana Lucic; Megan Stanley; Johannes Brandstetter; Patrick Garvan; Maik Riechert; Jonathan A. Weyn; Haiyu Dong; Anna Vaughan; Jayesh K. Gupta; Kit Thambiratnam; et al. (CoRR, 2024)

A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks. - Thomas Schmied; Thomas Adler; Vihang Patil; Maximilian Beck; Korbinian Pöppel; Johannes Brandstetter; Günter Klambauer; Razvan Pascanu; Sepp Hochreiter (CoRR, 2024)

NeuralDEM - Real-time Simulation of Industrial Particulate Flows. - Benedikt Alkin; Tobias Kronlachner; Samuele Papa; Stefan Pirker; Thomas Lichtenegger; Johannes Brandstetter (CoRR, 2024)

Vision-LSTM: xLSTM as Generic Vision Backbone. - Benedikt Alkin; Maximilian Beck; Korbinian Pöppel; Sepp Hochreiter; Johannes Brandstetter (ICLR, 2025)

MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Masked Image Modeling Representations. - Benedikt Alkin; Lukas Miklautz; Sepp Hochreiter; Johannes Brandstetter (ICLR, 2025)

Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences. - Niklas Schmidinger; Lisa Schneckenreiter; Philipp Seidl; Johannes Schimunek; Pieter-Jan Hoedt; Johannes Brandstetter; Andreas Mayr; Sohvi Luukkonen; Sepp Hochreiter; Günter Klambauer (ICLR, 2025)

Neural-Parareal: Self-improving acceleration of fusion MHD simulations using time-parallelisation and neural operators. - Stanislas Pamela; Naomi Carey; Johannes Brandstetter; Rob J. Akers; Lorenzo Zanisi; James Buchanan; Vignesh Gopakumar; Matthias Hoelzl; Guido Huijsmans; Kamran Pentland; James Tickner; G. Antonucci (Comput. Phys. Commun., 2025)

Envisioning better benchmarks for machine learning PDE solvers. - Johannes Brandstetter (Nat. Mac. Intell., 2025)

A foundation model for the Earth system. - Cristian Bodnar; Wessel P. Bruinsma; Ana Lucic; Megan Stanley; Anna Allen; Johannes Brandstetter; Patrick Garvan; Maik Riechert; Jonathan A. Weyn; Haiyu Dong; Jayesh K. Gupta; Kit Thambiratnam; et al. (Nat., 2025)

AB-UPT: Scaling Neural CFD Surrogates for High- Fidelity Automotive Aerodynamics Simulations via Anchored- Branched Universal Physics Transformers. - Benedikt Alkin; Maurits Bleeker; Richard Kurle; Tobias Kronlachner; Reinhard Sonnleitner; Matthias Dorfer; Johannes Brandstetter (Trans. Mach. Learn. Res., 2025)

5D Neural Surrogates for Nonlinear Gyrokinetic Simulations of Plasma Turbulence. - Gianluca Galletti; Fabian Paischer; Paul Setinek; William Hornsby; Lorenzo Zanisi; Naomi Carey; Stanislas Pamela; Johannes Brandstetter (CoRR, 2025)

NeuralCFD: Deep Learning on High-Fidelity Automotive Aerodynamics Simulations. - Maurits Bleeker; Matthias Dorfer; Tobias Kronlachner; Reinhard Sonnleitner; Benedikt Alkin; Johannes Brandstetter (CoRR, 2025)

LaM-SLidE: Latent Space Modeling of Spatial Dynamical Systems via Linked Entities. - Florian Sestak; Artur P. Toshev; Andreas Fürst; Günter Klambauer; Andreas Mayr; Johannes Brandstetter (CoRR, 2025)

Generative Topology Optimization: Exploring Diverse Solutions in Structural Design. - Andreas Radler; Eric Volkmann; Johannes Brandstetter; Arturs Berzins (CoRR, 2025)

Shape Generation via Weight Space Learning. - Maximilian Plattner; Arturs Berzins; Johannes Brandstetter (CoRR, 2025)

Towards scientific machine learning for granular material simulations - challenges and opportunities. - Marc Fransen; Andreas Fürst; Deepak Tunuguntla; Daniel N. Wilke; Benedikt Alkin; Daniel Barreto; Johannes Brandstetter; Miguel Angel Cabrera; Xinyan Fan; Mengwu Guo; Bram Kieskamp; Krishna Kumar; et al. (CoRR, 2025)

SIMSHIFT: A Benchmark for Adapting Neural Surrogates to Distribution Shifts. - Paul Setinek; Gianluca Galletti; Thomas Gross; Dominik Schnürer; Johannes Brandstetter; Werner Zellinger (CoRR, 2025)

Einstein Fields: A Neural Perspective To Computational General Relativity. - Sandeep Suresh Cranganore; Andrei Bodnar; Arturs Berzins; Johannes Brandstetter (CoRR, 2025)

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations. - Fabian Paischer; Gianluca Galletti; William Hornsby; Paul Setinek; Lorenzo Zanisi; Naomi Carey; Stanislas Pamela; Johannes Brandstetter (CoRR, 2025)

AB-UPT for Automotive and Aerospace Applications. - Benedikt Alkin; Richard Kurle; Louis Serrano; Dennis Just; Johannes Brandstetter (CoRR, 2025)

PAINT: Parallel-in-time Neural Twins for Dynamical System Reconstruction. - Andreas Radler; Vincent Seyfried; Stefan Pirker; Johannes Brandstetter; Thomas Lichtenegger (CoRR, 2025)

Towards Multi-Fidelity Scaling Laws of Neural Surrogates in CFD. - Paul Setinek; Gianluca Galletti; Johannes Brandstetter (CoRR, 2025)