Is the end of Insight in Sight ?

• URL: http://arxiv.org/abs/2505.04627v2
• Date: Wed, 04 Jun 2025 16:57:55 GMT
• Title: Is the end of Insight in Sight ?
• Authors: Jean-Michel Tucny, Mihir Durve, Sauro Succi,
• Abstract summary: A physics-informed neural network (PINN) trained on a rarefied gas dynamics problem governed by the Boltzmann equation.<n>Despite the system's clear structure and well-understood governing laws, the trained network's weights resemble Gaussian-distributed random matrices.<n>This suggests that deep learning and traditional simulation may follow distinct cognitive paths to the same outcome.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rise of deep learning challenges the longstanding scientific ideal of insight - the human capacity to understand phenomena by uncovering underlying mechanisms. In many modern applications, accurate predictions no longer require interpretable models, prompting debate about whether explainability is a realistic or even meaningful goal. From our perspective in physics, we examine this tension through a concrete case study: a physics-informed neural network (PINN) trained on a rarefied gas dynamics problem governed by the Boltzmann equation. Despite the system's clear structure and well-understood governing laws, the trained network's weights resemble Gaussian-distributed random matrices, with no evident trace of the physical principles involved. This suggests that deep learning and traditional simulation may follow distinct cognitive paths to the same outcome - one grounded in mechanistic insight, the other in statistical interpolation. Our findings raise critical questions about the limits of explainable AI and whether interpretability can - or should-remain a universal standard in artificial reasoning.

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