A Stochastic Porous Media Schr{ö}dinger Equation: Feynman-type Motivation, Well-Posedness and Control Interpretation
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Bibliographic record
Abstract
This paper's aim is threefold. First, using Feynman's path approach to the derivation of theclassical Schr{ö}dinger's equation in [6] and by introducing a slight path (or wave) dependency ofthe action, we derive a new class of equations of Schr{ö}dinger type where the driving operatoris no longer the Laplace one but rather of complex porous media-type. Second, using suitableconcepts of monotonicity in the complex setting and on appropriate functional spaces, we showthe existence and uniqueness of the solution to this type of equation. In the formulation of ourequation, we adjoin possible measurement absolute errors translating in an additive Brownianperturbation and interactions between different waves translating in a mean-field (or McKean-Vlasov) dependency of drift coefficient. Finally, using Fitzpatrick's characterization of maximalmonotone operators (cf. [7]), we propose a Br{é}zis-Ekeland type characterization of the solutionof the deterministic equation via a control problem. This is envisaged as a possible way toovercome strict monotonicity requirements in the complex setting.
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Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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