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Record W3136877468 · doi:10.1139/cjp-2021-0006

A neural network approach for solution of the Schrödinger equation for a particle in the Pöschl–Teller potential

2021· article· en· W3136877468 on OpenAlexvenueno aff
Garvit Arora, Apoorva Singh, Ashutosh Vishwa Bandhu

Bibliographic record

VenueCanadian Journal of Physics · 2021
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Applications
Canadian institutionsnot available
FundersUniversity of Delhi
KeywordsPhysicsArtificial neural networkSchrödinger equationWave functionGround stateEnergy (signal processing)Work (physics)Particle (ecology)QuantumApplied mathematicsQuantum mechanicsStatistical physicsArtificial intelligenceComputer scienceMathematics

Abstract

fetched live from OpenAlex

We use a shallow artificial neural network (ANN) to solve the Schrödinger equation and find the ground state energy and wavefunction for a particle in the Pöschl–Teller potential. We also use the finite difference method (FDM) to find these quantities and compare the results obtained using the two methods with the analytical results. Results obtained using the neural network are in good agreement with the analytical results. Our work reaffirms the promising nature of application of artificial neural networks in quantum mechanics.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score0.140

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)

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.

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

Opus teacher head0.047
GPT teacher head0.240
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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