TRIE benchmarks stochastic PDE surrogates on two chaotic SPDEs, finding generative models best match long-term statistics and uncertainty while latent versions cut inference time by 12x.
Flow match- ing for generative modeling
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
IFM conditions flow-matching velocity fields on patient history and planned treatments, using velocity-field Jacobian regularization to enforce signed, dose-bounded insulin-lowering and carbohydrate-raising effects on glucose in simulated UVA/Padova type 1 diabetes data.
Differentiable nonconformity scores induce flows that sample conformal prediction set boundaries, and mixing flows across levels produces conformal predictive distributions whose quantiles match the sets.
citing papers explorer
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TRIE: An Evaluation Framework for Stochastic PDE Surrogates
TRIE benchmarks stochastic PDE surrogates on two chaotic SPDEs, finding generative models best match long-term statistics and uncertainty while latent versions cut inference time by 12x.
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Interventional Flow Matching: Prospective Dose-Response Forecasting with Velocity-Field Jacobian Regularization
IFM conditions flow-matching velocity fields on patient history and planned treatments, using velocity-field Jacobian regularization to enforce signed, dose-bounded insulin-lowering and carbohydrate-raising effects on glucose in simulated UVA/Padova type 1 diabetes data.
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Flow-Based Conformal Predictive Distributions
Differentiable nonconformity scores induce flows that sample conformal prediction set boundaries, and mixing flows across levels produces conformal predictive distributions whose quantiles match the sets.