MMP.18:11 - SoTA-Echoing

Interoperable components still need a coupling construction. The Modelica Association’s Functional Mock-up Interface 3.0.2, §§3–4, distinguishes exchanging model equations from co-simulation and leaves the coordinating solver to the importer. This supports :4.4: an interface format does not select the numerical interaction or establish its error. Reusing existing simulators remains valuable when their exchange capabilities support the required result; a joint solve is the alternative when independently advanced components do not. Specification.

Choose a map by what it preserves. The current preCICE documentation, “Mapping configuration,” distinguishes sum-preserving maps, constant-field interpolation and weighted integral preservation. The adopted move in :4.2 is to derive the required map property from the exchanged quantity. The documentation’s mesh algorithms are useful specialized realizations, not universal choices for every representation. A cheap nearest-neighbor map can suffice when its error and preserved quantities fit the use. Source.

A common unknown needs a coherent joint distribution. Goudie and colleagues, Joining and splitting models with Markov melding (2019; arXiv v3), §3, constructs combinations through shared variables and addresses inconsistent marginal priors. This supplies a serious alternative to informal transfer of fitted summaries. The simple common-prior case in :4.5 and :5.2 is adopted here; more general pooling requires its additional assumptions. Read version. Manderson and Goudie, Combining chains of Bayesian models with Markov melding (2023), extends the construction to distinct shared quantities along a chain while retaining their dependence. That extension matters when one shared variable cannot represent all interfaces; an arbitrary network or dependent evidence still requires its own joint construction. Source.

Scale interfaces can need learned or derived closures. Sanderse and colleagues, Scientific machine learning for closure models in multiscale problems: a review (2024), §2, locates the missing contribution when reduction and evolution do not commute. That result supports :4.3’s return to MMP.9 or a suitable surrogate. A learned closure is one candidate alongside derived memory, retained state and sufficient bounds; its isolated fit does not settle its coupled use. Source.

Revisit the coupling when a component, exchange representation, shared data, scale or receiving result changes. A new numerical or learned method is useful when it improves that same coupling under its applicability conditions and available resources.