Benedikt Alkin

According to our database, Benedikt Alkin authored at least 12 papers between 2024 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

AB-UPT for Automotive and Aerospace Applications.

Benedikt Alkin, Richard Kurle, Louis Serrano, Dennis Just, Johannes Brandstetter

CoRR, October, 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, John Morrissey, Jonathan Nuttall, Jin Y. Ooi, Luisa Orozco, Stefanos-Aldo Papanicolopulos, Tongming Qu, Dingena Schott, Takayuki Shuku, WaiChing Sun, Thomas Weinhart, Dongwei Ye, Hongyang Cheng

CoRR, April, 2025

NeuralCFD: Deep Learning on High-Fidelity Automotive Aerodynamics Simulations.

Maurits Bleeker, Matthias Dorfer, Tobias Kronlachner, Reinhard Sonnleitner, Benedikt Alkin, Johannes Brandstetter

CoRR, February, 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

MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Masked Image Modeling Representations.

Benedikt Alkin, Lukas Miklautz, Sepp Hochreiter, Johannes Brandstetter

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Vision-LSTM: xLSTM as Generic Vision Backbone.

Benedikt Alkin, Maximilian Beck, Korbinian Pöppel, Sepp Hochreiter, Johannes Brandstetter

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

NeuralDEM - Real-time Simulation of Industrial Particulate Flows.

Benedikt Alkin, Tobias Kronlachner, Samuele Papa, Stefan Pirker, Thomas Lichtenegger, Johannes Brandstetter

CoRR, 2024

One Initialization to Rule them All: Fine-tuning via Explained Variance Adaptation.

Fabian Paischer, Lukas Hauzenberger, Thomas Schmied, Benedikt Alkin, Marc Peter Deisenroth, Sepp Hochreiter

CoRR, 2024

Universal Physics Transformers.

Benedikt Alkin, Andreas Fürst, Simon Schmid, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter

CoRR, 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: A Framework For Efficiently Scaling Neural Operators.

Benedikt Alkin, Andreas Fürst, Simon Schmid, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget.

Johannes Lehner, Benedikt Alkin, Andreas Fürst, Elisabeth Rumetshofer, Lukas Miklautz, Sepp Hochreiter

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024