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31 to 40 of 337 Results
README.md
Oct 14, 2026 - Replication Data for: Fast Karhunen-Loève Expansions via FFT-Accelerated Toeplitz Operators
Markdown Text - 10.7 KB - MD5: cce60b17134ad2056a1ec615a9cd6ab6
repeated_measurements.csv
Oct 14, 2026 - Replication Data for: Fast Karhunen-Loève Expansions via FFT-Accelerated Toeplitz Operators
Tabular Data - 2.8 KB - 12 Variables, 40 Observations - UNF:6:raoZQsTle+vBV/+RSbgK3g==
reproduce_all.jl
Oct 14, 2026 - Replication Data for: Fast Karhunen-Loève Expansions via FFT-Accelerated Toeplitz Operators
Unknown - 3.2 KB - MD5: a6ba816dac0c6db88336c37b9a11a146
tol_sensitivity.csv
Oct 14, 2026 - Replication Data for: Fast Karhunen-Loève Expansions via FFT-Accelerated Toeplitz Operators
Tabular Data - 1.2 KB - 8 Variables, 30 Observations - UNF:6:wCul2Ah23lWQPf612QQmrA==
Replication Data for: Efficient Confidence Interval Computation for Physics-Aware Machine Learning of Diffusion-Sorption Models
Oct 7, 2026
Scheurer, Stefania; Frenner, Riccardo; Bruennette, Tim; Oladyshkin, Sergey; Nowak, Wolfgang, 2026, "Replication Data for: Efficient Confidence Interval Computation for Physics-Aware Machine Learning of Diffusion-Sorption Models", https://doi.org/10.18419/DARUS-5967, DaRUS, V1, UNF:6:+gEES8LITw2YkntYAMuceg== [fileUNF]
Supplementary code to replicate key experiments from our paper. The code computes confidence intervals for the Finite Volume Neural Network (FINN), a physics-aware ML model that learns unknown parts of a PDE (here, the retardation function of a diffusion-sorption model). The workflow has four steps: 1. Train FINN on the observations. 2. Learn an em...
ac_generator.py
Oct 7, 2026 - Replication Data for: Efficient Confidence Interval Computation for Physics-Aware Machine Learning of Diffusion-Sorption Models
Python Source Code - 4.4 KB - MD5: 43e32a250e3144ed9e21645ea4b603bf
burgers_generator.py
Oct 7, 2026 - Replication Data for: Efficient Confidence Interval Computation for Physics-Aware Machine Learning of Diffusion-Sorption Models
Python Source Code - 4.8 KB - MD5: f1df6b03e55343e4e6f033d13e9f782c
c_langmuir.npy
Oct 7, 2026 - Replication Data for: Efficient Confidence Interval Computation for Physics-Aware Machine Learning of Diffusion-Sorption Models
Unknown - 406.6 KB - MD5: 1a9ae3589f0622bd2f0059496366f06c
config.py
Oct 7, 2026 - Replication Data for: Efficient Confidence Interval Computation for Physics-Aware Machine Learning of Diffusion-Sorption Models
Python Source Code - 6.0 KB - MD5: 72b922375baddece06e6aef461e216f0
config.py
Oct 7, 2026 - Replication Data for: Efficient Confidence Interval Computation for Physics-Aware Machine Learning of Diffusion-Sorption Models
Python Source Code - 211 B - MD5: 51f7155ff31312467bef971ef2172630
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