Pith. Sign in

RECORD

Combing physics-based and data-driven predictions for quantitatively accurate models that extrapolate well; with application to DIII-D, AUG, and ITER tokamak fusion reactors

PaperPDF from arXiv

data files

Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
X Bluesky LinkedIn Reddit Hacker News
Joseph Abbate, Emiliano Fable, Giovanni Tardini, Rainer Fischer, Egemen Kolemen, ASDEX Upgrade Team

Plasma Physics · Submitted 30 Sep 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Methodologies for combining the accuracy of data-driven models with extrapolability of physics-based models are described and tested, for the task of building transport models of tokamak fusion reactors that extrapolate well to new operational regimes.

how it is stated · asserted 0.99 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv ID2409.20256 (version 1)
Pith ID5QMGSFWM, Pith's permanent record of this paper and its review
CitationASDEX Upgrade team, Egemen Kolemen, Emiliano Fable, Giovanni Tardini, Joseph Abbate, Rainer Fischer. "Combing physics-based and data-driven predictions for quantitatively accurate models that extrapolate well; with application to DIII-D, AUG, and ITER tokamak fusion reactors." arXiv:2409.20256 (2024). https://pith.science/paper/5QMGSFWM
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,024 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.07 · human participants 0.03 · controlled comparison 0.10 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.21 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Methodologies for combining the accuracy of data-driven models with extrapolability of physics-based models are described and tested, for the task of building transport models of tokamak fusion reactors that extrapolate well to new operational regimes. Information from multiple physics simulations (the ASTRA transport code with gyro-Bohm and TGLF estimates for turbulent diffusion) as well as multiple distinct experiments (DIII-D and AUG tokamaks) are considered. Applications of the methodology to the task of commissioning and controlling a new reactor such as ITER are discussed.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Methodologies for combining the accuracy of data-driven models with extrapolability of physics-based models are described and tested, for the task of building transport models of tokamak fusion reactors that extrapolate well to new operational regimes. contribution 0.99 0.82 0.28 asserted 0.99
s1 Information from multiple physics simulations (the ASTRA transport code with gyro-Bohm and TGLF estimates for turbulent diffusion) as well as multiple distinct experiments (DIII-D and AUG tokamaks) are considered. method 0.99 0.66 0.04 asserted 0.94
s2 Applications of the methodology to the task of commissioning and controlling a new reactor such as ITER are discussed. interpretation 0.56 0.41 0.11 asserted 0.55

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.70.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Methodologies for combining the accuracy of data-driven models with extrapolability of physics-based models are described and tested, for the task of building transport models of tokamak fusion reactors that extrapolate well to new operational regimes. Information from multiple physics simulations (the ASTRA transport code with gyro-Bohm and TGLF estimates for turbulent diffusion) as well as multiple distinct experiments (DIII-D and AUG tokamaks) are considered. Applications of the methodology to the task of commissioning and controlling a new reactor such as ITER are discussed.

Human review

How Pith checks papers → · Full audit of this paper →

How the literature uses it

What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 2 sentences without a confident label

Uses the method or tool of 1 paper
  • Such a model must solve a logistic regression problem, i.e. learning scalar weights $α_predictor$ corresponding to each predictor that sum to one, [equation] PyTorch [ref] is employed to solve the regression problem for the optimal $n_predictors-1$ learnable parameters $α_i$, with the same loss as for the machine learning models (mean squared error on the normalized profiles).

    PyTorch: An Imperative Style, High-Performance Deep Learning Library · Meta-learning · 1.00

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.