# Microwave Ablation

Planning microwave ablation needs a 3D temperature field, but solving the coupled electromagnetic-thermal equations point by point is slow. GAPPTANet feeds an analytical specific absorption rate field in as an explicit heat-source prior, uses a geometry-aware adapter to correct applicator-induced near-field approximation errors, and reconstructs global heat diffusion and local high-temperature boundaries with a gated dual-branch decoder. On 27,050 high-fidelity simulations it reaches a full-field root-mean-square error of 0.6001 degrees Celsius and a batch-amortized GPU forward pass of 1.58 ms per sample, outperforming convolutional baselines and three neural-operator architectures under the same test setting.

Planning microwave ablation needs a 3D temperature field, but solving the coupled electromagnetic-thermal equations point by point is slow. GAPPTANet feeds an analytical specific absorption rate field in as an explicit heat-source prior, uses a geometry-aware adapter to correct applicator-induced near-field approximation errors, and reconstructs global heat diffusion and local high-temperature boundaries with a gated dual-branch decoder. On 27,050 high-fidelity simulations it reaches a full-field root-mean-square error of 0.6001 degrees Celsius and a batch-amortized GPU forward pass of 1.58 ms per sample, outperforming convolutional baselines and three neural-operator architectures under the same test setting.