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Proposes an attention-based, physics-guided convolutional neural network as a surrogate model to predict microstructural evolution in systems governed by the Cahn-Hilliard equation, demonstrating stable and accurate long-time predictions.
A research paper introducing Three-Phase Transformer (3PT), which applies Tesla's polyphase geometry to transformer architectures by organizing the residual stream into three 120° offset phases. The approach achieves 7.2% perplexity improvement on WikiText-103 with minimal parameters (0.00124% overhead) and 1.93× convergence speedup.