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arxiv: 2604.14240 · v1 · submitted 2026-04-15 · 💻 cs.AI · cs.LG· stat.ML

Recognition: unknown

Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making

Benoit Gaudou, Joseph Morlier, Julien Aligon, Koji Shimoyama, Moncef Garouani, Muhammad Daffa Robani, Nicolas Verstaevel, Paul Saves, Pramudita Satria Palar

Authors on Pith no claims yet

Pith reviewed 2026-05-10 13:46 UTC · model grok-4.3

classification 💻 cs.AI cs.LGstat.ML
keywords surrogate modelingexplainable AIXAIsimulationblack-box modelsinterpretabilitycomplex systemsdecision making
0
0 comments X

The pith

Integrating explainable AI techniques into surrogate modeling workflows can make opaque simulations interpretable for decision-making.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Surrogate models accelerate complex system simulations but often hide the relationships between inputs and physical outputs. The paper shows how XAI methods can be mapped to each stage of surrogate creation and application to uncover these relationships. This matters for fields like engineering and science where understanding model behavior is essential for reliable use. The survey uses examples from various simulations to demonstrate strengths in revealing interactions. It also flags challenges and sets an agenda for making explainability a standard part of these workflows.

Core claim

This survey reconnects surrogate modeling and XAI by providing a structured perspective that maps existing XAI techniques onto the various stages of surrogate modeling workflows for design and exploration. Drawing on illustrative applications from equation-based simulations and agent-based modeling, it highlights techniques for revealing interactions and supporting comprehension. The work identifies open challenges such as explainability of dynamical systems and mixed-variable systems, and proposes a research agenda to embed explainability as a core element from model construction through decision-making.

What carries the argument

A structured mapping of XAI techniques onto surrogate modeling workflow stages that reconnects the fields and embeds explainability in simulation processes.

If this is right

  • Adapted XAI tools can unpack how input variables drive responses in surrogates.
  • Human comprehension of complex behaviors improves through highlighted interactions.
  • A research agenda prioritizes explainability in dynamical and mixed-variable systems.
  • Practitioners gain actionable insights beyond just faster simulations.
  • Explainability becomes embedded from construction to decision-making.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This mapping could extend to improve reliability in safety-critical applications by verifying surrogate explanations against known physics.
  • Implementing the proposed agenda in agent-based models might uncover previously hidden emergent behaviors.
  • Linking this to uncertainty quantification methods could provide more robust decision support.
  • A direct test would involve applying the mapping to a real engineering simulator and assessing if decision quality improves.

Load-bearing premise

That existing XAI techniques can be adapted to engineering-specific constraints such as highly correlated inputs, dynamical systems, and rigorous reliability requirements while preserving their explanatory power and without introducing new failure modes.

What would settle it

A concrete test would be to apply the mapped XAI techniques to a surrogate model of a known dynamical system with correlated inputs and verify whether the generated explanations match independent physical analysis or introduce inaccuracies.

read the original abstract

The simulation of complex systems increasingly relies on sophisticated but fundamentally opaque computational black-box simulators. Surrogate models play a central role in reducing the computational cost of complex systems simulations across a wide range of scientific and engineering domains. Notwithstanding, they inevitably inherit and often exacerbate this black-box nature, obscuring how input variables drive physical responses. Conversely, Explainable Artificial Intelligence (XAI) offers powerful tools to unpack these models. Yet, XAI methods struggle with engineering-specific constraints, such as highly correlated inputs, dynamical systems, and rigorous reliability requirements. Consequently, surrogate modeling and XAI have largely evolved as distinct fields of research, despite their strong complementarity. To reconnect these approaches, this state-of-the-art survey provides a structured perspective that maps existing XAI techniques onto the various stages of surrogate modeling workflows for design and exploration. To ground this synthesis, we draw upon illustrative applications across both equation-based simulations and agent-based modeling. We survey a broad spectrum of techniques, highlighting their strengths for revealing interactions and supporting human comprehension. Finally, we identify pressing open challenges, including the explainability of dynamical systems and the handling of mixed-variable systems, and propose a research agenda to make explainability a core, embedded element of simulation-driven workflows from model construction through decision-making. By transforming opaque emulators into explainable tools, this agenda empowers practitioners to move beyond accelerating simulations to extracting actionable insights from complex system behaviors.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The paper is a state-of-the-art survey that maps XAI techniques onto the stages of surrogate modeling workflows for design and exploration in complex system simulations. It draws on examples from equation-based and agent-based modeling to illustrate how XAI can reveal input-output interactions, surveys strengths of various techniques for human comprehension, identifies open challenges (e.g., dynamical systems explainability, mixed-variable handling, correlated inputs, reliability), and proposes a research agenda to embed explainability as a core element from model construction through decision-making.

Significance. If the mapping and agenda are comprehensive, the survey could usefully reconnect the surrogate modeling and XAI communities by organizing existing methods around engineering workflows and highlighting domain-specific constraints. It provides a forward-looking synthesis rather than new methods, which is valuable for guiding research on transforming opaque simulators into actionable tools, especially given the acknowledged struggles of current XAI with engineering requirements.

minor comments (2)
  1. The abstract states that XAI methods 'struggle with' engineering constraints but does not specify how the proposed agenda would avoid introducing new failure modes; a brief discussion of this risk in the challenges section would strengthen the forward-looking claims.
  2. The survey claims to be 'structured' and 'grounded' in illustrative applications, but the abstract provides no detail on the literature selection criteria or coverage validation; adding a methods subsection on search strategy and inclusion criteria would improve reproducibility of the synthesis.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of our survey and the recommendation for minor revision. The referee's summary accurately reflects the paper's structure, scope, and contributions in mapping XAI methods to surrogate modeling workflows for simulations.

Circularity Check

0 steps flagged

No circularity: pure literature survey with no derivations or self-referential reductions

full rationale

This is a state-of-the-art survey paper whose central contribution is a structured mapping of existing XAI techniques onto surrogate modeling workflow stages, plus identification of open challenges. No equations, fitted parameters, predictions, or uniqueness theorems appear in the provided text or abstract. The work draws on external literature for illustrative applications and does not reduce any claim to quantities defined by the authors' own prior results or by construction. Self-citations, if present, are not load-bearing for any derivation because the paper performs descriptive synthesis rather than proving new results. The derivation chain is therefore empty and self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a survey paper whose claims rest on the authors' selection and interpretation of prior literature; it introduces no free parameters, mathematical axioms, or new postulated entities.

pith-pipeline@v0.9.0 · 5603 in / 1216 out tokens · 36273 ms · 2026-05-10T13:46:19.935346+00:00 · methodology

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Reference graph

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