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Record W54696906 · doi:10.1007/0-306-47015-2_14

Modelling Molecules in Intense Laser Fields - Numerical Methods for an IBM-SP2 Parallel Computer Environment

2005· book-chapter· en· W54696906 on OpenAlexaff
André D. Bandrauk, Huizhong Lu

Bibliographic record

VenueKluwer Academic Publishers eBooks · 2005
Typebook-chapter
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsSupercomputerFemtosecondPhysicsIBMComputationLaserElectromagnetismComputational scienceComputer scienceQuantum mechanicsAlgorithmParallel computingOptics

Abstract

fetched live from OpenAlex

The behaviour, control and manipulation of molecules exposed to ultrashort (femtosecond) intense (>10**14 W/cm2) is of current interest to the new femtosecond technology including ultrafast electronic and electromagnetic technologies. The theoretical modelling and simulation of the interaction of molecules with the new ultashort and intense laser pulses available today requires large scale computations as one is dealing with nonlinear nonperturbative phenomena coupling two fundamental partial differential equations, pdf, s, the hyperbolic Maxwell equations of electromagnetism and the quantum equations of Schroedinger and Dirac for the molecules. Most of our work in this area involving nonperturbative solutions of these equations which are of interest to modem photochemistry and photophysics has been performed on IBM-SP2, s with 16 processors at CACPUS and up to a hundred processors at the Maui High Performance Computing Center. Special high order numerical methods, based on split operator methods and large matrix Lanczos diagonaliztion preocedures have been developped to treat nonperturbatively such laser-molecule systems. We will describe in the present paper these new methods, their advantages over previous ones, their limitations and possible improvements compatible with new supercomputer technologies.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.634
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.315
Teacher spread0.274 · 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.

Study designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2005
Admission routes1
Has abstractyes

Explore more

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