Bibliographic record
Abstract
This chapter addresses the reader who wishes to explore the theory and practice of time reversal, and to obtain an overview of the concepts, definitions, and applications of time reversal methods. Having no natural sense of negative time flow, we must resort to our imagination to describe what happens to physical phenomena, notably the behavior of waves in space and time, when time is reversed. The meaning of time reversal in mechanics and wave propagation is discussed in general terms, and the nature of one‐dimensional causal and acausal electromagnetic waves is explained by means of computer visualizations. The concept of the time reversal mirror as an active transmitter of time‐reversed incident fields is demonstrated and extended to two‐ and three‐dimensional scenarios. The principles of radiating and scattering source reconstruction in both open and closed ergodic structures are introduced and illustrated by means of typical modeling examples. Classical and algorithmic formulations of time reversal in terms of Green's functions lead to an interpretation of time reversal focusing in space and time as a correlation process. Superresolution as a consequence of multiple scattering or multipath propagation is demonstrated. A selected list of references containing seminal papers, books, reviews, and applications of time reversal in different areas of acoustics, electromagnetics, and optics concludes the chapter. The references provide in‐depth coverage of the fundamental concepts presented in this chapter and will guide the reader toward application‐specific extensions of the theory and numerical models for advanced researchers and practitioners of time reversal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".