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Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences

Redefining Super-Resolution:Fine-mesh PDE predictions without classical simulations

Rajat Sarkar · Ritam Majumdar · Vishal Jadhav · Sagar Srinivas Sakhinana · Venkataramana Runkana


In Computational Fluid Dynamics (CFD), coarse mesh simulations offer computational efficiency but often lack precision. Applying conventional super-resolution to these simulations poses a significant challenge due to the fundamental contrast between downsampling high-resolution images and authentically emulating low-resolution physics. The former method conserves more of the underlying physics, surpassing the usual constraints of real-world scenarios. We propose a novel definition of super-resolution tailored for PDE-based problems. Instead of simply downsampling from a high-resolution dataset, we use coarse-grid simulated data as our input and predict fine-grid simulated outcomes. Employing a physics-infused UNet upscaling method, we demonstrate its efficacy across various 2D-CFD problems such as discontinuity detection in Burger's equation, Methane combustion, and fouling in Industrial heat exchangers. Our method enables the generation of fine-mesh solutions bypassing traditional simulation, ensuring considerable computational saving and fidelity to the original ground truth outcomes. Through diverse boundary conditions during training, we further establish robustness of our method, paving the way for its broad applications in engineering and scientific CFD solvers.

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