Physics-guided Convolutional Neural Network for Domain Growth Prediction in Systems with Conserved Kinetics
arXiv:2606.26128v1 Announce Type: new Abstract: The spatiotemporal evolution of many physical, chemical, and biological systems is described by nonlinear partial differential equations (PDEs). Recently, deep neural network-based surrogate models have gained increasing interest as efficient alternatives to computationally expensive traditional numerical solvers. In this work, we propose an attention-based, physics-guided convolutional neural network as a surrogate model to learn the microstructural evolution of such systems. We train the model to accurately predict the full time-evolution of ph
This development reflects the accelerating trend of applying sophisticated AI techniques like physics-guided neural networks to complex scientific and engineering problems, driven by advancements in deep learning and computational power.
A strategic reader should care because this represents a foundational step towards more accurate, efficient, and rapid simulations of critical physical systems, impacting fields from materials science to climate modeling.
This research introduces an attention-based, physics-guided convolutional neural network capable of predicting the full time-evolution of microstructural systems, potentially reducing reliance on computationally intensive traditional numerical solvers.
- · Materials science researchers
- · Chemical engineers
- · Drug discovery sector
- · AI/ML researchers
- · Traditional numerical solver developers (limited impact)
- · R&D cycles relying solely on high-cost physical experimentation
The immediate effect is a reduction in computational time and resources required for complex scientific simulations.
This could accelerate materials discovery and optimization, leading to faster innovation in industries dependent on new material properties.
Long-term, such advancements could enable highly efficient 'digital twin' simulations for entire industrial processes or even climate systems, offering unprecedented predictive capabilities.
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Read at arXiv cs.LG