Bibliographic record
Abstract
These lectures present highlights of spectral theory for selfadjoint partial differential operators, emphasizing problems with discrete spectrum. Spectral methods permeate the theory of partial differential equations. Linear PDEs are often solved by separation of variables, getting eigenvalues when the spectrum is discrete and continuous spectrum when it is not. Further, linearized stability of a steady state or traveling wave of a nonlinear PDE depends on the sign of the first eigenvalue, or more generally on the location of the spectrum in the complex plane. We define eigenvalues in terms of quadratic forms on a general Hilbert space. Particular applications include the eigenvalues of the Laplacian under Dirichlet and Neumann boundary conditions. Rayleigh-type principles characterize the first and higher eigenvalues, and lead to a number of comparison and domain monotonicity properties. Lastly, the role of eigenvalues in stability analysis is investigated for a reaction-diffusion equation in one spatial dimension. Computable examples are presented before the general theory. Some ideas are used before being properly defined, but overall students gain a better understanding of the purpose of the theory by gaining first a solid grounding in specific examples.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".