arXiv:2607.00196v1 Announce Type: new Abstract: Many scientific systems exhibit uncertainty from stochastic forcing, unresolved degrees of freedom, or imperfect observations, making reliable surrogate forecasting fundamentally distributional rather than pointwise. For such systems, deterministic neural surrogates fail to capture statistical measures and forecast uncertainty. We introduce TRIE, an evaluation framework for stochastic PDE surrogates that asks whether models reproduce invariant measures, provide trustworthy predictive uncertainty, and scale to efficient probabilistic generation. W
The increasing complexity of scientific systems with inherent uncertainties and the growing adoption of AI in scientific discovery necessitate more robust evaluation frameworks for stochastic models.
This framework addresses a critical limitation of current deterministic AI models in scientific applications by enabling the development and assessment of AI that can accurately capture and predict uncertainty, which is vital for reliability and trustworthiness.
The ability to reliably evaluate AI surrogates for stochastic systems moves from ad-hoc methods to a structured framework, improving the accuracy and trustworthiness of AI in fields like climate modeling, drug discovery, and materials science.
- · AI researchers in scientific computing
- · Scientific simulation software providers
- · Industries relying on predictive modeling (e.g., aerospace, pharmaceuticals)
- · Developers of purely deterministic AI surrogates
- · Organizations relying on opaque or unreliable predictive models
Improved reliability and applicability of AI in scientific research involving stochastic processes.
Accelerated discovery of new materials, drugs, or climate models due to more trustworthy AI predictions.
Enhanced collaboration between AI experts and domain scientists, leading to more integrated and high-impact scientific breakthroughs.
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Read at arXiv cs.LG