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Applying Radial Basis Functions and Partition of Unity for Solving Heating Equations Optimal Control Issues

2024· article· en· 0 citations· W4405945852 on OpenAlex· 10.18280/mmep.111218

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian venueIt was published in a Canadian venue.

No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Full frame distilled prediction

Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

Candidate categories
none
Consensus categories
none
Domain
Candidate signal: noneConsensus signal: none
Study design
Candidate signal: Simulation or modelingConsensus signal: Simulation or modeling
Genre
Candidate signal: MethodsConsensus signal: none
Teacher disagreement score
0.626
Threshold uncertainty score
0.513
Validation status
machine_predicted_unvalidated · codex-gemma-dda1882f352a

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Opus teacher head0.080
GPT teacher head0.312
Teacher spread
0.232 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

In this study, we suggest applying the Partition of Unity approach using Radial Basis Functions (RBF-PU) towards the solution of heat equation-governed sparse optimal control issues.An 2 norm is included in the goal function to encourage sparseness in the control equation and quadratic coefficients are used to reduce the deviations from a desired state.Efficient processing of spatially sparse controllers is made possible by this combination, which is crucial for numerous practical uses.By splitting the domain into overlapped subdomains and performing local RBF approximation, which is then integrated utilizing compactly maintained weight functions, the RBF-PU technique offers a versatile and effective strategy.The correctness and effectiveness of the suggested strategy are demonstrated numerically, showing how it can be used to solve intricate optimum control issues with larger dimensions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

The record

Venue
Mathematical Modelling and Engineering Problems
Topic
Differential Equations and Numerical Methods
Field
Mathematics
Canadian institutions
not available
Funders
not available
Keywords
Partition of unityPartition (number theory)Basis (linear algebra)MathematicsRadial basis functionOptimal controlApplied mathematicsMathematical analysisMathematical optimizationComputer sciencePhysicsGeometryThermodynamicsFinite element methodArtificial intelligenceCombinatorics
Has abstract in OpenAlex
yes