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teaching:ws2024:dl4science:papers [2025/02/06 22:58] (current)
Karnik Ram created |
| | Seminar: Deep Learning for the Natural Sciences (5 ECTS) |
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| | Winter Semester 2024 |
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| | == List of potential papers == |
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| | ^No. ^Paper ^ |
| | | 1 | [[https://openreview.net/forum?id=B73niNjbPs|Continuous PDE Dynamics Forecasting with Implicit Neural Representations]]| |
| | | 2 | [[https://arxiv.org/abs/2108.08481|Neural Operator: Learning Maps Between Function Spaces |
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| | | 3 | [[https://arxiv.org/abs/2401.11037|Equivariant Graph Neural Operator for Modeling 3D Dynamics |
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| | | 4 | [[https://www.nature.com/articles/s41467-022-29939-5|E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials]]| |
| | | 5 | [[https://www.nature.com/articles/s41467-023-36329-y|Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics |
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| | | 6 | [[https://arxiv.org/abs/2210.06662|Action Matching: Learning Stochastic Dynamics from Samples |
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| | | 7 | [[https://openreview.net/forum?id=xuY33XhEGR|ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs]]| |
| | | 8 | [[https://arxiv.org/abs/2312.15796|GenCast: Diffusion-based ensemble forecasting for medium-range weather |
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| | | 9 | [[https://arxiv.org/abs/2405.13063|Aurora: A Foundation Model of the Atmosphere |
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| | | 10 | [[https://openreview.net/forum?id=vN9fpfqoP1|Fine-Tuned Language Models Generate Stable Inorganic Materials as Text]]| |
| | | 11 | [[https://openreview.net/forum?id=W4pB7VbzZI|FlowMM: Generating Materials with Riemannian Flow Matching]]| |
| | | 12 | [[https://openreview.net/forum?id=5Z3GURcqwT|Spherical Channels for Modeling Atomic Interactions]]| |
| | | 13 | [[https://arxiv.org/abs/2312.03687|MatterGen: a generative model for inorganic materials design]]| |
| | | 14| [[https://arxiv.org/abs/2405.04967|MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures]] | |
| | | 15 | [[https://www.nature.com/articles/s41586-023-06735-9|Scaling deep learning for materials discovery |
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| | | 16 | [[https://www.nature.com/articles/s41586-021-03819-2|Highly accurate protein structure prediction with AlphaFold 2]] | |
| | | 17 | [[https://www.nature.com/articles/s41586-024-07487-w|Accurate structure prediction of biomolecular interactions with AlphaFold 3]] | |
| | | 18 | [[https://openreview.net/forum?id=kKF8_K-mBbS|DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking]]| |
| | | 19 | [[https://openreview.net/forum?id=yQcebEgQfH|AlphaFold Meets Flow Matching for Generating Protein Ensembles |
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| | | 20 | [[https://arxiv.org/abs/2106.12594|Real-time gravitational-wave science with neural posterior estimation]]| |
| | | 21 | [[https://arxiv.org/abs/2404.09636|All-in-one simulation-based inference]] | |
| | | 22 | [[https://arxiv.org/abs/2306.11925|LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching]]| |
| | | 23 | [[https://openaccess.thecvf.com/content/CVPR2024/html/Stevens_BioCLIP_A_Vision_Foundation_Model_for_the_Tree_of_Life_CVPR_2024_paper.html|BioCLIP: A Vision Foundation Model for the Tree of Life]]| |
| | | 24 | [[https://arxiv.org/abs/2408.10205|KAN 2.0: Kolmogorov-Arnold Networks Meet Science |
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| | | 25 | [[https://arxiv.org/abs/2006.11287|Discovering Symbolic Models from Deep Learning with Inductive Biases |
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| | | 26 | [[https://openreview.net/forum?id=vJx6fld6l0|Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics]] | |
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