PN 6

SimTech EXC 2075 Project Network 6 "Machine learning for simulation"

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1 to 10 of 12 Results

Studying Dynamical Systems for Intelligible Modeling and Unconventional Computing(Universität Stuttgart) May 20, 2026 We investigate computation through the lens of dynamical systems, unifying physical processes and machine learning. By treating both hardware and algorithms as evolving dynamical systems, we leverage natural physical dynamics - such as relaxation to stable states and phase transitions - as a foundation for robust, efficient computation. Our work sh...
Coarse-grained non-equilibrium dynamics with generative machine learning(Universität Stuttgart) Dec 15, 2025
PN 6-8 (II)(Universität Stuttgart) Mar 28, 2025 Spatiotemporal ensembles often result from physical simulations. These ensembles contain many information-rich members, each corresponding to different simulation input parameters. The extensive data size makes manual analysis infeasible, necessitating automated approaches to assist the analysis. In the preceding project (PN 6-8 (I)), methods and t...
PN 6-15(Universität Stuttgart) Nov 20, 2024 This Dataverse contains replication data and visualizations from Project Network 6-15: Machine Learning and Reservoir Computing with Many-Body Dynamics. The project is part of SimTech’s Project Network 6, Machine Learning for Simulation (https://www.simtech.uni-stuttgart.de/exc/research/pn/pn6/ ). The Dataverse includes simulations of non-equilibri...
Hard Negative Captions(Universität Stuttgart) Jul 3, 2024PN 6-5 (II)
PN 6-5 (II)(Universität Stuttgart) Jul 3, 2024 SimTech Project PN 6-5 (II) "Interpretable and explainable cognitive inspired machine learning systems"
PN 6A-3(Universität Stuttgart) Mar 8, 2024 Advanced learning strategies for potential energy surfaces applied to organic electrolytes
PN 6A-4(Universität Stuttgart) Mar 8, 2024 Unified diagnostic evaluation of physics-based, data-driven and hybrid hydrological models based on information theory
Data and Code for: Meta-Uncertainty in Bayesian Model Comparison(Universität Stuttgart) May 17, 2023 Meta-Uncertainty represents a fully probabilistic framework for quantifying the uncertainty over Bayesian posterior model probabilities (PMPs) using meta-models. Meta-models integrate simulated and observed data into a predictive distribution for new PMPs and help reduce overconfidence and estimate the PMPs in future replication studies.
PN 6-6(Universität Stuttgart) Sep 29, 2021 SimTech Project PN 6-6 "Machine Learning for Data-driven Visualization"

Studying Dynamical Systems for Intelligible Modeling and Unconventional Computing(Universität Stuttgart)

May 20, 2026

We investigate computation through the lens of dynamical systems, unifying physical processes and machine learning. By treating both hardware and algorithms as evolving dynamical systems, we leverage natural physical dynamics - such as relaxation to stable states and phase transitions - as a foundation for robust, efficient computation. Our work sh...

Coarse-grained non-equilibrium dynamics with generative machine learning(Universität Stuttgart)

Dec 15, 2025

PN 6-8 (II)(Universität Stuttgart)

Mar 28, 2025

Spatiotemporal ensembles often result from physical simulations. These ensembles contain many information-rich members, each corresponding to different simulation input parameters. The extensive data size makes manual analysis infeasible, necessitating automated approaches to assist the analysis. In the preceding project (PN 6-8 (I)), methods and t...

PN 6-15(Universität Stuttgart)

Nov 20, 2024

This Dataverse contains replication data and visualizations from Project Network 6-15: Machine Learning and Reservoir Computing with Many-Body Dynamics. The project is part of SimTech’s Project Network 6, Machine Learning for Simulation (https://www.simtech.uni-stuttgart.de/exc/research/pn/pn6/ ). The Dataverse includes simulations of non-equilibri...

Hard Negative Captions(Universität Stuttgart)

Jul 3, 2024PN 6-5 (II)

PN 6-5 (II)(Universität Stuttgart)

Jul 3, 2024

SimTech Project PN 6-5 (II) "Interpretable and explainable cognitive inspired machine learning systems"

PN 6A-3(Universität Stuttgart)

Mar 8, 2024

Advanced learning strategies for potential energy surfaces applied to organic electrolytes

PN 6A-4(Universität Stuttgart)

Mar 8, 2024

Unified diagnostic evaluation of physics-based, data-driven and hybrid hydrological models based on information theory

Data and Code for: Meta-Uncertainty in Bayesian Model Comparison(Universität Stuttgart)

May 17, 2023

Meta-Uncertainty represents a fully probabilistic framework for quantifying the uncertainty over Bayesian posterior model probabilities (PMPs) using meta-models. Meta-models integrate simulated and observed data into a predictive distribution for new PMPs and help reduce overconfidence and estimate the PMPs in future replication studies.

PN 6-6(Universität Stuttgart)

Sep 29, 2021

SimTech Project PN 6-6 "Machine Learning for Data-driven Visualization"

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