| date | 2025-08-21 |
|---|
This appendix collects assorted references and curiosities that pair well with this course. Items are grouped by theme; brief annotations indicate how you might use each link. Some links may require institutional access or sign-in.
- Buffon's Needle (Monte Carlo demo): Short post with code and derivation; nice for geometric probability and basic MC error analysis.
https://simonensemble.github.io/posts/2018-04-11-buffon/ - Gallery of Curves: Visual encyclopedia of classic planar curves; quick inspiration for parametric plotting.
https://en.wikipedia.org/wiki/Gallery_of_curves - Differential Geometry of Surfaces (Wikipedia): Definitions and formulas you can consult when working with meshes and curvature.
https://en.wikipedia.org/wiki/Differential_geometry_of_surfaces - Why do golf balls have dimples? (COMSOL blog): Boundary layer, drag, and turbulence primer; good for discussion on modeling limits.
https://www.comsol.com/blogs/why-do-golf-balls-have-dimples
- Supervised Machine Learning for Science (open book): Science-oriented ML text with examples; good companion reading.
https://ml-science-book.com/ - Surrogates: Gaussian Process Modeling, Design, and Optimization: Reference on GP modeling and design of experiments. (N. G. Gramacy, Routledge, 2020).
https://www.routledge.com/Surrogates-Gaussian-Process-Modeling-Design-and-Optimization-for-the/Gramacy/p/book/9780367415426 - Stan Reference Manual: Canonical documentation for probabilistic modeling and inference.
https://mc-stan.org/docs/reference-manual/index.html - Understanding Deep Learning (open book): Rigorous DL text that complements our ML units.
https://udlbook.github.io/udlbook/
- SymbolicRegression.jl (Stable docs): Tools for fitting closed-form expressions to data (Julia).
https://ai.damtp.cam.ac.uk/symbolicregression/dev/ - SciCode-Bench: Benchmark for scientific code generation; context for LLMs that write science code.
https://scicode-bench.github.io/ - How chaotic is chaos?: Perspective on over-claiming accuracy for chaotic systems; useful for limits-of-prediction discussion.
https://www.stochasticlifestyle.com/how-chaotic-is-chaos-how-some-ai-for-science-sciml-papers-are-overstating-accuracy-claims/
- MolCalc: Browser-based molecule builder with quick property estimates; lightweight for ideas and sanity checks.
https://molcalc.org/ - AlphaFold Protein Structure Database (EBI): Canonical database for predicted structures.
https://alphafold.ebi.ac.uk/ - AlphaFold server (3rd-party interface): Convenience interface; not the canonical EBI database.
https://alphafoldserver.com/about - DeepMind blog: Millions of new materials discovered with deep learning: Overview and pointers for GNoME-style materials discovery.
https://deepmind.google/discover/blog/millions-of-new-materials-discovered-with-deep-learning/ - Microsoft Research: AI meets materials discovery: Feature on AI-accelerated pipelines (e.g., MatterSim/MatterGen).
https://www.microsoft.com/en-us/research/story/ai-meets-materials-discovery/
- AlphaGeometry: Olympiad-level geometry solving; framing for automated theorem proving.
https://deepmind.google/discover/blog/alphageometry-an-olympiad-level-ai-system-for-geometry/ - AI solves IMO problems at silver-medal level: Broader context on problem-solving systems.
https://deepmind.google/discover/blog/ai-solves-imo-problems-at-silver-medal-level/
- Unbiasing fermionic quantum Monte Carlo with a quantum computer. Nature 603, 416–420 (2022). DOI: 10.1038/s41586-021-04351-z.
https://www.nature.com/articles/s41586-021-04351-z - "ChatGPT is bullshit." Ethics and Information Technology 26, 38 (2024). DOI: 10.1007/s10676-024-09775-5.
https://link.springer.com/article/10.1007/s10676-024-09775-5 - Computing hydration free energies of small molecules with first-principles accuracy. arXiv (2024–2025). DOI: 10.48550/arXiv.2405.18171. (See highlight: "Computing solvation free energies…", Comp. Chem. Highlights, July 2025.)
https://arxiv.org/abs/2405.18171
http://www.compchemhighlights.org/2025/07/computing-solvation-free-energies-of.html?m=1
- Generative AI Teaching Activities (WUSTL CTL): Vetted classroom activities and assignments.
https://ctl.wustl.edu/resources/generative-ai-teaching-activities-online-repository/ - Teaching Naked (prompts & compendium): Ready-to-use prompts/handouts for course design.
https://teachingnaked.com/prompts/
- Fundamentals of Numerical Computation (Julia): Modern numerics text with worked examples.
https://tobydriscoll.net/fnc-julia/home.html - PEP 8 — A foolish consistency is the hobgoblin of little minds: Style guidance and when to deviate pragmatically.
https://peps.python.org/pep-0008/#a-foolish-consistency-is-the-hobgoblin-of-little-minds - A practical introduction to LLMs in Python (ebook/notes): Applied overview; useful for quick prototypes.
https://pointbreezepubs.gumroad.com/l/llm
- MIT 8.334 Statistical Mechanics II (playlist): Advanced stat-mech enrichment.
https://www.youtube.com/playlist?list=PLUl4u3cNGP63HkEHvYaNJiO0UCUmY0Ts7 - YouTube short — materials/heat-treating clip (quick curiosity).
https://www.youtube.com/shorts/W2xxT3b-4H0 - YouTube short — science explainer (quick curiosity).
https://www.youtube.com/shorts/Wc7Q2UJ3WtE - General-audience coverage: NYT — "AI Test: Humanity's Last Exam" (Jan 23, 2025). Paywalled.
https://www.nytimes.com/2025/01/23/technology/ai-test-humanitys-last-exam.html - Google Research — Imagen: Text-to-image diffusion models (for visualization discussions).
https://imagen.research.google/
- LinkedIn post (Justin Hodges): Access and visibility may vary.
https://www.linkedin.com/posts/justin-hodges-phd-3432a58b_physics-statistics-math-ugcPost-7293143917791694848-3fxk/ - Cornell Chem 7870 homework: Reference assignment on PDE solvers; content may change.
https://github.com/cornell-chem-7870/chem-7870-2025-pde-solvers-homework_10 - LinkedIn post (Andrew Rosen): Access and visibility may vary.
https://www.linkedin.com/posts/andrew-s-rosen_while-im-a-big-proponent-of-general-purpose-activity-7359219267877130242-2Zeb/?rcm=ACoAAATbG_QBvs2odhZQvbGP20f5rd_uxTwal8c
- Links are intentionally eclectic; use them for inspiration, enrichment, and discussion.
- If you notice a dead link, please open an issue or PR in the course repository: https://github.com/wexlergroup/comp-prob-solv.