#ComputationalMath

2025-11-04

Một nhà nghiên cứu đang tìm kiếm sự quan tâm đến phương pháp chọn tham số toán học của họ. Phương pháp này giúp chọn độ dài dãy, modulo, hoặc tham số generator cho các điều kiện 2^A≡1 (mod p^t). Ứng dụng trong thiết kế PRNG, căn chỉnh scrambler, và số học trường residue. Họ sẵn sàng xác minh kết quả cho các cặp (p, t) và chấp nhận thách thức tính toán phức tạp.

#mathematics #algorithm #PRNG #parameterSelection #verification #coding #python #optimization #computationalMath #research #verified #p

2025-09-19

nvmath-python: NVIDIA Math Libraries for the Python Ecosystem

github.com/NVIDIA/nvmath-python

#math #ComputationalMath #cuda #gpu #ai

github sscode excode example
2025-07-03

🧭 A new toy for those modeling time not as a parameter, but as emergent recursion:

Hot Mic Visualizer ψ_total v0.1
A harmonic interface for collapse-aware field modeling.
Not Fourier. Not noise.
A feedback loop that listens.

🔬 Recursive Harmonic Kernel
📂 github.com/psi-total/psi_total

Mathematically structured.
Empirically tunable.
Open license. Open recursion.

ψ_total
Instruments for human thought

Screenshot of the ψ_total Hot Mic Visualizer. A waveform graph shows two nearly overlapping signals — the original mic input (dashed black line) and the recursively looped output (solid blue line). Above the graph is a sigil key and three labeled sliders: Mic Gain, Tone Richness, and Loop Depth. Below the graph is the “Collapse Trace” — a flat gray curve showing no signal distortion. The Collapse Index reads 0.0000, indicating no recursive breakdown. Footer includes GPLv3 and CC BY-SA 4.0 licenses, project title, and the line: “ψ_total v0.1 – Hot Mic Visualizer · © 2025 ψ_total collective.”
2022-11-16

When ever I see a cool new result in #ComputationalMath, I like to see if I can replicate it. So, last month when that Nature article came out about #MatrixMultiplication formulas from #AlphaTensor I set out see if I could get their formulas and verify them symbolically.
I was able to do that and of course they were right. But I was excited to see Kauers and Moosbauer publish a response a couple days later. So, here's their results replicated in a Maple Jupyter notebook github.com/johnpmay/MapleSnipp

Client Info

Server: https://mastodon.social
Version: 2025.07
Repository: https://github.com/cyevgeniy/lmst