Sentence embeddings reduce reconstruction error by 81% in Darcy-flow inversion by providing categorical geological constraints, with limited added value from within-class text detail.
Water Resources Research 55, 703–728
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Latent diffusion model parameterization allows MCMC and SMC to outperform latent-space ESMDA in data mismatch and uncertainty reduction for 3D subsurface DA, while model-space ESMDA produces unrealistic posteriors.
KL-DNN uses low-rank SVD and nested Karhunen-Loeve expansions to enable scalable operator learning on large 3D GCS simulations, achieving 0.04% relative pressure error and two-order speedup over DeepONet.
citing papers explorer
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What Do Language Priors Contribute to Darcy-Flow Inversion? A Mechanistic Audit
Sentence embeddings reduce reconstruction error by 81% in Darcy-flow inversion by providing categorical geological constraints, with limited added value from within-class text detail.
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Data assimilation for subsurface flow using latent diffusion model parameterization: performance of ensemble-Kalman and Monte Carlo techniques
Latent diffusion model parameterization allows MCMC and SMC to outperform latent-space ESMDA in data mismatch and uncertainty reduction for 3D subsurface DA, while model-space ESMDA produces unrealistic posteriors.
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A Trainable-by-Parts Operator Learning Framework: Bridging DeepONet and Karhunen-Loeve Expansions for Large-Scale Applications
KL-DNN uses low-rank SVD and nested Karhunen-Loeve expansions to enable scalable operator learning on large 3D GCS simulations, achieving 0.04% relative pressure error and two-order speedup over DeepONet.