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Papers

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Individual papers cited across the site, gathered by area. Each is one a page leans on directly — a founding result, a definitive treatment, or a well-documented failure mode.

Numerics and V&V

  • Williams, Waterman & Patterson (2009), Roofline: an insightful visual performance model — HPC
  • Salari & Knupp (2000), Code verification by the method of manufactured solutions — V&V
  • Daly (2006), A higher order estimate of the optimum checkpoint interval — Data and I/O
  • Tóth (2000), The \(\nabla\cdot B = 0\) constraint in shock-capturing MHD codes — MHD

Fluids, aerodynamics, turbulence

  • Chorin (1968), Numerical solution of the Navier–Stokes equations — Incompressible
  • Menter (1994), Two-equation eddy-viscosity turbulence models for engineering applications — RANS
  • Germano et al. (1991), A dynamic subgrid-scale eddy viscosity model — LES
  • Nicoud & Ducros (1999), Subgrid-scale stress modelling based on the square of the velocity gradient tensor (WALE) — LES
  • Jameson (1988), Aerodynamic design via control theory — Adjoints
  • Giles & Pierce (2000), An introduction to the adjoint approach to design — Adjoints
  • Zhang (2000), A flexible new technique for camera calibration — Classical CV

Meteorology and wildfire

  • Hersbach et al. (2020), The ERA5 global reanalysis — Observations
  • Rothermel (1972), A mathematical model for predicting fire spread in wildland fuels — Fire weather
  • Potter (2012), Atmospheric interactions with wildland fire behaviour (two-part review) — Fire weather
  • Srock et al. (2018), The Hot-Dry-Windy Index — Fire weather
  • Giglio et al. (2016), The Collection 6 MODIS active fire detection algorithm — Scientific imagery
  • Hoskins, McIntyre & Robertson (1985), On the use and significance of isentropic potential vorticity maps — Dynamics

Machine learning

  • Vaswani et al. (2017), Attention Is All You Need — Architectures
  • He et al. (2016), Deep Residual Learning for Image Recognition — Architectures
  • Ronneberger et al. (2015), U-Net: Convolutional Networks for Biomedical Image Segmentation — Architectures
  • Loshchilov & Hutter (2019), Decoupled Weight Decay Regularization (AdamW) — Training craft
  • Kingma & Welling (2014), Auto-Encoding Variational Bayes — Generative
  • Goodfellow et al. (2014), Generative Adversarial Networks — Generative
  • Ho et al. (2020), Denoising Diffusion Probabilistic Models — Generative
  • Rombach et al. (2022), High-Resolution Image Synthesis with Latent Diffusion Models — Generative
  • Lin et al. (2017), Focal Loss for Dense Object Detection — Detection
  • Carion et al. (2020), End-to-End Object Detection with Transformers — Detection
  • Kirillov et al. (2023), Segment Anything — Detection
  • Radford et al. (2021), Learning Transferable Visual Models From Natural Language Supervision (CLIP) — VLMs
  • Lewis et al. (2020), Retrieval-Augmented Generation — LLMs
  • Hu et al. (2021), LoRA: Low-Rank Adaptation of Large Language Models — LLMs
  • Liu et al. (2023), Lost in the Middle — LLMs

Evaluation and methodology

  • Kaufman et al. (2012), Leakage in data mining: formulation, detection, and avoidance — Evaluation
  • Guo et al. (2017), On calibration of modern neural networks — Evaluation
  • Saito & Rehmsmeier (2015), The precision-recall plot is more informative than the ROC plot — Evaluation
  • Grinsztajn et al. (2022), Why do tree-based models still outperform deep learning on tabular data? — Classical ML
  • Ploton et al. (2020), Spatial validation reveals poor predictive performance of large-scale ecological mapping models — Scientific imagery
  • Sculley et al. (2015), Hidden Technical Debt in Machine Learning Systems — MLOps
  • Wilson et al. (2017), Good enough practices in scientific computing — Research craft
  • Crameri, Shephard & Heron (2020), The misuse of colour in science communication — Research craft

Scientific ML

  • Raissi, Perdikaris & Karniadakis (2019), Physics-informed neural networks — PINNs
  • Krishnapriyan et al. (2021), Characterizing possible failure modes in physics-informed neural networks — PINNs
  • Wang, Teng & Perdikaris (2021), Understanding and mitigating gradient pathologies in PINNs — PINNs
  • Li et al. (2021), Fourier Neural Operator for Parametric Partial Differential Equations — Neural operators
  • Lu, Jin & Karniadakis (2021), Learning nonlinear operators via DeepONet — Neural operators
  • Kovachki et al. (2023), Neural Operator: Learning Maps Between Function Spaces — Neural operators
  • Pfaff et al. (2021), Learning Mesh-Based Simulation with Graph Networks — Mesh GNNs
  • Sanchez-Gonzalez et al. (2020), Learning to Simulate Complex Physics with Graph Networks — Mesh GNNs
  • Battaglia et al. (2018), Relational inductive biases, deep learning, and graph networks — Mesh GNNs
  • Ling, Kurzawski & Templeton (2016), Reynolds-averaged turbulence modelling using deep neural networks with embedded invariance — Hybrid coupling
  • Duraisamy, Iaccarino & Xiao (2019), Turbulence modeling in the age of data — Hybrid coupling
  • Um et al. (2020), Solver-in-the-loop — Hybrid coupling
  • Benner, Gugercin & Willcox (2015), A survey of projection-based model reduction methods — Surrogates
  • Kennedy & O'Hagan (2001), Bayesian calibration of computer models — UQ

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