A one-step flow matching model using transformer in VAE latent space with non-Gaussian source and auxiliary networks generates accurate high-resolution path-dependent stress fields, achieving 6-7x CPU and ~100x GPU speedup over FEM.
Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager
3 Pith papers cite this work. Polarity classification is still indexing.
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A coupled LSTM-GNN model reconstructs local elasto-plastic stress fields from macroscopic loading paths on a plate-with-hole microstructure, achieving 1000x speedup and mesh transferability with 1.9% error.
A delay integro-differential equation model fitted to pharmacokinetic and RNA-seq data indicates that a single medium-to-high dose of pembrolizumab may suffice for effective neoadjuvant eradication of locally advanced MSI-H/dMMR colorectal tumors.
citing papers explorer
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One-Step Flow Matching for Generative Modeling of Path-Dependent Physical Fields
A one-step flow matching model using transformer in VAE latent space with non-Gaussian source and auxiliary networks generates accurate high-resolution path-dependent stress fields, achieving 6-7x CPU and ~100x GPU speedup over FEM.
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Non-linear mechanical field reconstruction coupling recurrent neural networks with physics-informed graph neural networks
A coupled LSTM-GNN model reconstructs local elasto-plastic stress fields from macroscopic loading paths on a plate-with-hole microstructure, achieving 1000x speedup and mesh transferability with 1.9% error.