arXiv:2606. 23757v1 Announce Type: cross Abstract: Extracting interpretable governing equations from sparse, noisy chemical time-series data remains difficult because discrete reaction topology and continuous kinetic parameters are tightly coupled.
Simulation-based Bayesian inference for Chemical Reaction Networks (CRNs) must simultaneously solve two computationally demanding problems: discriminating between overlapping reaction pathways and est...
arXiv:2502. 18966v2 Announce Type: replace Abstract: General chemical reaction conditions that achieve consistently high performance across multiple substrates are important for practical applications such as library synthesis and high-throughput experimentation.
arXiv:2504. 01894v2 Announce Type: replace Abstract: We present a bifidelity method for uncertainty quantification of parameter estimates in complex systems, leveraging generative models trained to sample the target conditional distribution.
PosteriorBench is a new benchmark that evaluates how well generative inverse solvers recover full posterior distributions rather than just a single reconstruction. It tests four physics-based inverse problems—Darcy flow inversion, Poisson source recovery, carbon capture and storage, and light transport material inference—using high-fidelity reference posteriors generated by rejection sampling and MCMC. The benchmark employs five metrics (posterior-mean error, posterior-standard-deviation error, maximum mean discrepancy, sliced Wasserstein distance, and radially averaged power-spectrum error) to assess pointwise accuracy, uncertainty, distributional alignment, and global frequency fidelity, revealing significant distribution-matching gaps in current solvers and highlighting the importance of neural operators, guidance weights, and generation noise for posterior-variance calibration.
arXiv:2407. 20432v3 Announce Type: replace Abstract: Bayesian inference methods such as Markov Chain Monte Carlo (MCMC) typically require repeated computations of the likelihood function, but in some scenarios this is infeasible and alternative methods are needed.
arXiv:2604. 02121v2 Announce Type: replace-cross Abstract: Stochastic kinetic models are ubiquitous in physics, yet inferring their parameters from experimental data remains challenging.
KPPax is a new kinetic preprocessor that converts atmospheric chemistry mechanisms written in the Kinetic PreProcessor (KPP) language into differentiable, just‑in‑time compiled, vectorisable code in JAX. It evaluates rate laws in log space to maintain differentiability and integrates stiff systems using implicit solvers with forward‑ and reverse‑mode adjoints from the diffrax library, enabling exact sensitivities without hand‑derived adjoint code. Demonstrations with the POLLU benchmark show that KPPax can predict which rate coefficients are identifiable and improve neural‑network corrections of emission inventories, reducing errors by up to a factor of five.
The paper explores how to use gradients of simulator outputs with respect to input parameters to improve Bayesian inference for expensive simulators. By incorporating gradient information into Gaussian process surrogate models, the authors show that Bayesian optimization can converge faster under a limited simulation budget. They find that reverse‑mode differentiation yields efficiency gains, while forward‑mode does not offset its computational cost, especially when the number of parameters exceeds the output dimensionality.
arXiv:2607. 06252v1 Announce Type: cross Abstract: Many problems in science and engineering are difficult to model accurately, either due to unknown physical mechanisms, poorly quantified measurement uncertainty, or prohibitive computational costs of high-fidelity simulations.
The paper proposes pre-training uncertainty estimators on large, diverse materials science datasets to improve uncertainty quantification (UQ) for machine‑learned interatomic potentials (MLIPs). By evaluating these pre‑trained models on both in‑distribution and out‑of‑domain adsorption‑energy predictions, the authors demonstrate that zero‑shot UQ is viable and that fine‑tuning yields strong correlations between predicted uncertainty and actual prediction error, even on challenging datasets. This approach aims to provide foundational UQ models that enable broader, more reliable deployment of MLIPs across chemical space.
arXiv:2610.19074v1 Announce Type: new Abstract: Reliable uncertainty quantification remains an important challenge in AI. Hierarchical Bayesian methods provide a principled framework for this by repr...