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This paper investigates whether sentence embeddings can serve as an inference-time interface for injecting geological knowledge into a learned Darcy-flow inverse solver, finding that text conditioning reduces reconstruction error by 81% relative to a no-text counterfactual, with most gains from categorical class-level constraints.
This paper introduces the degeneracy distillery, a method that automatically detects and resolves degenerate parameter combinations in physical models by estimating and flattening the Fisher information matrix, reducing the simulation budget required for neural posterior estimation while providing physical insight.
This thread discusses the concept of 'Jagged Intelligence' in AI, framing it as a consequence of AI learning being an ill-posed inverse problem, and argues that external stabilizers like scaffolding and verification are essential.
Introduces Decoupled Latent Optimization (DLO) for full waveform inversion, which relaxes latent optimization into a quadratic-penalty objective, outperforming classical and diffusion-based methods on benchmarks while preserving smoothed-velocity initialization.
This paper proposes a history-bootstrapped autoregressive flow matching method for reconstructing full spatiotemporal fields (velocity and temperature) from partial observations of boiling dynamics, addressing the ill-posed inverse problem with non-Markovian posterior.
Proposes a variance-reduced zeroth-order Langevin sampling method for non-log-concave distributions, establishing the first non-asymptotic convergence guarantees, and applies it to inverse problems with score-based generative priors.
Proposes a hierarchical variational policy framework for reward-guided diffusion, enabling high-quality sampling with reduced inference cost. Achieves strong quality-speed tradeoff on tasks like super-resolution.
A comprehensive survey reviewing recent advances in using artificial intelligence to solve inverse partial differential equation (PDE) problems, covering inverse problems, inverse design, and control problems, with applications across scientific and industrial domains.
This paper introduces a new energy-based model for linear inverse problems that learns normalized posterior densities, overcoming limitations of diffusion models. It enables unbiased sampling, adaptive sampling, and blind degradation estimation, with competitive performance on ImageNet, CelebA, and AFHQ.
Steven Brunton announces his new book 'Optimization: A Bootcamp for Machine Learning, Inverse Problems, and Control', with pre-order available and accompanying free PDF, YouTube videos, and Python code.
This paper proposes NeTMY, an amortization-free coordinate neural field for inverse problems in NV-center quantum sensing, using a corrected forward model and sparse reconstruction losses to overcome center-collapse pathologies.
This paper presents a Bayesian inverse problem framework for rain field reconstruction using Commercial Microwave Links and Diffusion Model priors, demonstrating improved accuracy over existing baselines.
This paper analyzes zero-shot conditional sampling with pretrained diffusion models for linear inverse problems, providing information-theoretic guarantees and proposing a projected-Langevin initialization method.