Normalizing flows are constructed by learning the velocity of a stochastic interpolant via a quadratic loss derived from its probability current, yielding an efficient ODE-based alternative to diffusion models.
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13 Pith papers cite this work. Polarity classification is still indexing.
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Composed BDF schemes with complex coefficients increase order by one, supply order p+1 error estimates from the imaginary part, break the Dahlquist barrier up to order 8, and give step-ratio stability bounds for non-uniform meshes.
RedEigCD enables stable timestep increases up to 40 times larger than full-order models for projection-based ROMs of incompressible flows by using exact spectral bounds on reduced convective and diffusive operators together with a proof that ROM stable timesteps are at least as large as FOM ones.
SITN performs single-sample OOD detection via goodness-of-fit testing on noise samples in the factorised latent space of continuous normalizing flows.
Simulation-based inference with a Gaussian process emulator trained on ~1300 POSSIS simulations enables rapid, robust kilonova parameter estimation that avoids MCMC biases from likelihood misspecification.
Nowhere-vanishing Koopman eigenfunctions form a multiplicative group, enabling polynomial extensions from principal ones to enrich eigenspaces and enable global representations from local data in multistable systems.
Flow matching achieves single-step pixel accuracy and 20-step perceptual quality for Sentinel-2 super-resolution, outperforming diffusion and Real-ESRGAN while enabling large-scale 2.5 m land-cover products.
Polytropic stellar wind models are extended beyond extreme adiabatic cases to non-adiabatic localized heating, with added energy shown plausible relative to flares and relevant to solar wind observations.
BG-SINDy reformulates l0-constrained regression as term-level l2,0 regularization and uses progressive pruning guided by balance contributions to recover small-coefficient terms in multiscale PDEs.
Bayesian-ARGOS is a hybrid frequentist-Bayesian method that discovers equations from limited noisy observations more efficiently than SINDy or bootstrap-ARGOS while adding uncertainty quantification.
S-MNN reformulates Mechanistic Neural Networks to achieve linear computational complexity for long sequences while preserving accuracy and interpretability.
The Parker-Sochacki method delivers 4 to 13 orders of magnitude better kinetic energy conservation than Runge-Kutta methods for charged particle motion in static magnetic fields while running faster at matched accuracy.
Experimental imbibition data on four porous materials are pre-processed with a monotonicity-preserving fit and used to calibrate a PDE model of capillary absorption.
citing papers explorer
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Building Normalizing Flows with Stochastic Interpolants
Normalizing flows are constructed by learning the velocity of a stochastic interpolant via a quadratic loss derived from its probability current, yielding an efficient ODE-based alternative to diffusion models.
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Error estimation for numerical approximations of ODEs via composition techniques. Part II: BDF methods
Composed BDF schemes with complex coefficients increase order by one, supply order p+1 error estimates from the imaginary part, break the Dahlquist barrier up to order 8, and give step-ratio stability bounds for non-uniform meshes.
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Stable self-adaptive timestepping for Reduced Order Models for incompressible flows
RedEigCD enables stable timestep increases up to 40 times larger than full-order models for projection-based ROMs of incompressible flows by using exact spectral bounds on reduced convective and diffusive operators together with a proof that ROM stable timesteps are at least as large as FOM ones.
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The Signal in the Noise: OOD Detection Through Goodness-of-Fit Testing in Factorised Latent Spaces
SITN performs single-sample OOD detection via goodness-of-fit testing on noise samples in the factorised latent space of continuous normalizing flows.
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Rapid and robust simulation-based inference for kilonovae
Simulation-based inference with a Gaussian process emulator trained on ~1300 POSSIS simulations enables rapid, robust kilonova parameter estimation that avoids MCMC biases from likelihood misspecification.
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On the algebra of Koopman eigenfunctions and on some of their infinities
Nowhere-vanishing Koopman eigenfunctions form a multiplicative group, enabling polynomial extensions from principal ones to enrich eigenspaces and enable global representations from local data in multistable systems.
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Flow matching for Sentinel-2 super-resolution: implementation, application, and implications
Flow matching achieves single-step pixel accuracy and 20-step perceptual quality for Sentinel-2 super-resolution, outperforming diffusion and Real-ESRGAN while enabling large-scale 2.5 m land-cover products.
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Polytropic stellar wind models with strongly localized heating
Polytropic stellar wind models are extended beyond extreme adiabatic cases to non-adiabatic localized heating, with added energy shown plausible relative to flares and relevant to solar wind observations.
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Balance-Guided Sparse Identification of Multiscale Nonlinear PDEs with Small-coefficient Terms
BG-SINDy reformulates l0-constrained regression as term-level l2,0 regularization and uses progressive pruning guided by balance contributions to recover small-coefficient terms in multiscale PDEs.
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Fast and principled equation discovery from chaos to climate
Bayesian-ARGOS is a hybrid frequentist-Bayesian method that discovers equations from limited noisy observations more efficiently than SINDy or bootstrap-ARGOS while adding uncertainty quantification.
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Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning
S-MNN reformulates Mechanistic Neural Networks to achieve linear computational complexity for long sequences while preserving accuracy and interpretability.
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High-Accuracy Numerical Solutions of Particle Motion in Static Magnetic Fields
The Parker-Sochacki method delivers 4 to 13 orders of magnitude better kinetic energy conservation than Runge-Kutta methods for charged particle motion in static magnetic fields while running faster at matched accuracy.
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Data-Informed Mathematical Characterization of Absorption Properties in Artificial and Natural Porous Materials
Experimental imbibition data on four porous materials are pre-processed with a monotonicity-preserving fit and used to calibrate a PDE model of capillary absorption.