Kashinath et al., Physics-informed machine learning: Case studies for weather and climate modelling.
Willard et al., Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems, 2020.
Hajij et al., Topological Deep Learning: Going Beyond Graph Data, 2023.
Brehmer et al, Geometric Algebra Transformers, 2023.
Di Giovanni et al., Understanding convolution on graphs via energies, 2023.
Barbero et al., Sheaf Neural Networks with Connection Laplacians, 2022.
Topping et al., Understanding over-squashing and bottlenecks on graphs via curvature, 2022. Code: https://github.com/jctops/understanding-oversquashing. Post: https://towardsdatascience.com/over-squashing-bottlenecks-and-graph-ricci-curvature-c238b7169e16?sk=f5cf01cbd57b4fee8fb3a89d447e56a9.
Cesa et al., A Program to Build E(N)-Equivariant Steerable CNNs, 2022. Code: https://github.com/QUVA-Lab/escnn.
Di Giovanni et al., Heterogeneous manifolds for curvature-aware graph embedding, 2022.
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Suk et al., Mesh convolutional neural networks for wall shear stress estimation in 3D artery models, 2021. Code: https://github.com/sukjulian/coronary-mesh-convolution.
Bodnar et al., Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs, 2021. Code: https://github.com/twitter-research/neural-sheaf-diffusion.
Bodnar et al., Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks, 2021. Code: https://github.com/twitter-research/cwn.
Bodnar et al., Weisfeiler and Lehman Go Cellular: CW Networks, 2021. Code: https://github.com/twitter-research/cwn.
Satorras et al., E(n) Equivariant Graph Neural Networks, 2021.
de Haan et al., Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs, 2021. Code: https://github.com/Qualcomm-AI-research/gauge-equivariant-mesh-cnn.
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Gerken et al., Geometric Deep Learning and Equivariant Neural Networks, 2021.
Weiler et al., Coordinate Independent Convolutional Networks - Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds, 2021. Code: https://github.com/mauriceweiler/MobiusCNNs.
Hansen et al., Opinion Dynamics on Discourse Sheaves, 2020.
Hansen et al., Sheaf Neural Networks, 2020.
Cohen et al., Gauge Equivariant Convolutional Networks and the Icosahedral CNN, 2019.
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Risi Condor, N-body Networks: a Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentials
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Runge et al., Detecting and quantifying causal associations in large nonlinear time series datasets, 2019. Code: https://github.com/jakobrunge/tigramite. See also: https://www.nature.com/articles/s41467-019-10105-3.
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Quinting & Grams, EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 1: Development of deep learning model, 2022.
Paula Harder et al., Physics-Informed Learning of Aerosol Microphysics, 2022. https://github.com/paulaharder/aerosol-microphysics-emulation.
Keisler, Ryan, Forecasting Global Weather with Graph Neural Networks, 2022.
Kriegmair et al., Using neural networks to improve simulations in the gray zone, 2021.
Beucler et al., Climate-Invariant Machine Learning, 2021. Code: https://github.com/tbeucler/CBRAIN-CAM. (sic!)
L. Scheck, A neural network based forward operator for visible satellite images and its adjoint, 2021.
Hatfield et al., Building Tangent‐Linear and Adjoint Models for Data Assimilation With Neural Networks, 2021.
Giladi, Niv et al., Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling, 2021.
Tesch et al., Variant Approach for Identifying Spurious Relations That Deep Learning Models Learn, 2021.
Rodríguez-Fernández et al., SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact, 2019.
Beucler et al., Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems, 2019. Code: https://github.com/tbeucler/CBRAIN-CAM. (sic!)
Beucler et al., Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling, 2019. Code: https://github.com/tbeucler/CBRAIN-CAM. (sic!)
See also: https://github.com/ikespand/awesome-machine-learning-fluid-mechanics
Suk et al., Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall, 2022.
Li et al., Fourier neural operator for parametric partial differential equations, 2021. Code: https://github.com/zongyi-li/fourier_neural_operator.
Mohan et al., Wavelet-Powered Neural Networks for Turbulence, 2020.
Wang et al., Towards Physics-Informed Deep Learning for Turbulent Flow Prediction, 2020. Code: https://github.com/Rose-STL-Lab/Turbulent-Flow-Net.
Maulik et al., Sub-grid modelling for two-dimensional turbulence using neural networks, 2019.
Ling et al., Reynolds averaged turbulence modeling using deep neural networks with embedded invariance, 2016.
Brunton et al., Modern Koopman Theory for Dynamical Systems, 2021.
Champion et al., Data-driven discovery of coordinates and governing equations, 2019.
Jiang et al., MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework. Code: https://github.com/maxjiang93/space_time_pde.
Sanchez-Gonzalez et al., Learning to Simulate Complex Physics with Graph Networks, 2020.
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Walters et al., Trajectory Prediction using Equivariant Continuous Convolution, 2020. Code: https://github.com/Rose-STL-Lab/ECCO.
Cranmer et al., Discovering Symbolic Models from Deep Learning with Inductive Biases, 2020. Code: https://github.com/MilesCranmer/symbolic_deep_learning.
Cranmer et al., Lagrangian Neural Networks, 2020. Code: https://github.com/MilesCranmer/lagrangian_nns.
Serviansky et al., Set2Graph: Learning Graphs From Sets, 2020. Code: https://github.com/hadarser/SetToGraphPaper.
Veselkov et al., HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods, 2019. Code: https://bitbucket.org/iAnalytica/drugs_container_public/src/master.
Howard et al., Formal models of memory based on temporally-varying representations, 2022.
Howard et al., Cognitive computation using neural representations of time and space in the Laplace domain, 2020.