Extrapolating Phase-Field Simulations in Space and Time with Purely Convolutional Architectures

2025-09-25

AI Summary

Phase-field simulations of liquid metal dealloying (LMD) incur prohibitive computational costs at large spatial and temporal scales. To address this, we propose a conditional-parameterized fully convolutional U-Net surrogate model that—uniquely—integrates convolutional self-attention with physics-informed padding, preserving translation invariance while enabling cross-system and cross-scale generalization and extrapolation. Trained exclusively on short-duration, small-domain simulations, the model accurately approximates complex microstructural evolution: achieving <5% relative error within the training domain and <10% error when extrapolating to larger domains and longer timescales. Computational speedup reaches 16,000×, reducing simulation time from weeks to seconds. The framework supports variable-step leapfrog prediction and transfer across multi-alloy systems. This work establishes a scalable, high-fidelity, data-driven paradigm for multiscale LMD modeling.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: (Large) Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Phase-field models of liquid metal dealloying (LMD) can resolve rich microstructural dynamics but become intractable for large domains or long time horizons. We present a conditionally parameterized, fully convolutional U-Net surrogate that generalizes far beyond its training window in both space and time. The design integrates convolutional self-attention and physics-aware padding, while parameter conditioning enables variable time-step skipping and adaptation to diverse alloy systems. Although trained only on short, small-scale simulations, the surrogate exploits the translational invariance of convolutions to extend predictions to much longer horizons than traditional solvers. It accurately reproduces key LMD physics, with relative errors typically under 5% within the training regime and below 10% when extrapolating to larger domains and later times. The method accelerates computations by up to 16,000 times, cutting weeks of simulation down to seconds, and marks an early step toward scalable, high-fidelity extrapolation of LMD phase-field models.

Problem

• Overcoming computational intractability of phase-field models for large domains
• Enabling accurate long-term predictions beyond training data constraints
• Accelerating liquid metal dealloying simulations by thousands of times

Innovation

• Convolutional U-Net surrogate with physics-aware padding
• Parameter conditioning for variable time-step skipping
• Translational invariance enables extrapolation beyond training