New PI-DeepONet architecture slashes training costs for high-dimensional problems

Researchers have developed a modified architecture for physics-informed Deep Operator Networks (PI-DeepONets) that significantly reduces training costs for higher-dimensional problems. This new formulation, which mathematically equivalent to the original, optimizes tensor handling and reduces redundant computations in the Branch network. The proposed PI-DeepONet architecture achieves comparable accuracy to the original while demonstrating speed-ups of up to 3.75x in 2D experiments and making training feasible on hardware like the NVIDIA A100 GPU, which was previously limited by memory constraints. AI

IMPACT Enables application of physics-informed learning to more complex, higher-dimensional problems previously limited by computational cost.

RANK_REASON Research paper detailing a new implementation of a physics-informed neural network architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Client Networks
• Deep Operator Networks
• Knowledge Sharing in Audit Firms
• NVIDIA A100 GPU
• physics-informed neural networks
• PI-DeepONets
• Sergio Maldonado

• paper
• infra

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