Autonomous Adaptive Solver Selection for Chemistry Integration via Reinforcement Learning

• URL: http://arxiv.org/abs/2604.00264v1
• Date: Tue, 31 Mar 2026 21:44:58 GMT
• Title: Autonomous Adaptive Solver Selection for Chemistry Integration via Reinforcement Learning
• Abstract summary: We introduce a constrained reinforcement learning (RL) framework that autonomously selects between an implicit BDF integrator (CVODE) and a quasi-steady-state (QSS) solver during chemistry integration.<n>Across 0D homogeneous reactor conditions, the RL-adaptive policy achieves a mean speedup of approximately $3times$, with speedups ranging from $1.11times$ to $10.58times$.<n>Without retraining, the 0D-trained policy transfers to 1D counterflow diffusion flames over strain rates $10$--$2000mathrms-1$, delivering consistent $approx 2.2
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The computational cost of stiff chemical kinetics remains a dominant bottleneck in reacting-flow simulation, yet hybrid integration strategies are typically driven by hand-tuned heuristics or supervised predictors that make myopic decisions from instantaneous local state. We introduce a constrained reinforcement learning (RL) framework that autonomously selects between an implicit BDF integrator (CVODE) and a quasi-steady-state (QSS) solver during chemistry integration. Solver selection is cast as a Markov decision process. The agent learns trajectory-aware policies that account for how present solver choices influence downstream error accumulation, while minimizing computational cost under a user-prescribed accuracy tolerance enforced through a Lagrangian reward with online multiplier adaptation. Across sampled 0D homogeneous reactor conditions, the RL-adaptive policy achieves a mean speedup of approximately $3\times$, with speedups ranging from $1.11\times$ to $10.58\times$, while maintaining accurate ignition delays and species profiles for a 106-species \textit{n}-dodecane mechanism and adding approximately $1\%$ inference overhead. Without retraining, the 0D-trained policy transfers to 1D counterflow diffusion flames over strain rates $10$--$2000~\mathrm{s}^{-1}$, delivering consistent $\approx 2.2\times$ speedup relative to CVODE while preserving near-reference temperature accuracy and selecting CVODE at only $12$--$15\%$ of space-time points. Overall, the results demonstrate the potential of the proposed reinforcement learning framework to learn problem-specific integration strategies while respecting accuracy constraints, thereby opening a pathway toward adaptive, self-optimizing workflows for multiphysics systems with spatially heterogeneous stiffness.

Related papers

• Hybrid Iterative Neural Low-Regularity Integrator for Nonlinear Dispersive Equations [0.0]
  HIN-LRI is a hybrid framework that augments a classical numerical solver with a neural operator trained to correct the solver's structured truncation error.<n>A lightweight neural network, operating on a low-dimensional latent manifold, learns the residual defect that analytical methods cannot close.<n>Experiments show that HIN-LRI improves accuracy over analytical, splitting methods, and neural PDE surrogates, with stable spatial refinement, effective out-of-distribution transfer, and modest online overhead.
  arXiv  Detail & Related papers  (2026-05-06T12:50:36Z)
• A Numerical Method for Coupling Parameterized Physics-Informed Neural Networks and FDM for Advanced Thermal-Hydraulic System Simulation [0.0]
  Severe accident analysis using system-level codes such as MELCOR is indispensable for nuclear safety assessment.<n>Existing surrogate models accelerate these analyses but depend on large volumes of simulation data.<n> physics-informed neural networks (PINNs) enable data-free training but must be retrained for every change in problem parameters.
  arXiv  Detail & Related papers  (2026-04-03T02:50:58Z)
• Operator Learning Using Weak Supervision from Walk-on-Spheres [81.26322147849918]
  Training neural PDE solvers is often bottlenecked by expensive data generation or unstable physics-informed neural network (PINN)<n>We propose an alternative approach using Monte Carlo approaches to estimate the solution to the PDE as a process for weak supervision during training.
  arXiv  Detail & Related papers  (2026-03-01T17:23:39Z)
• ODAR: Principled Adaptive Routing for LLM Reasoning via Active Inference [60.958331943869126]
  ODAR-Expert is an adaptive routing framework that optimize the accuracy-efficiency trade-off via principled resource allocation.<n>We show strong and consistent gains, including 98.2% accuracy on MATH and 54.8% on Humanity's Last Exam.
  arXiv  Detail & Related papers  (2026-02-27T05:22:01Z)
• Optimal Unconstrained Self-Distillation in Ridge Regression: Strict Improvements, Precise Asymptotics, and One-Shot Tuning [61.07540493350384]
  Self-distillation (SD) is the process of retraining a student on a mixture of ground-truth and the teacher's own predictions.<n>We show that for any prediction risk, the optimally mixed student improves upon the ridge teacher for every regularization level.<n>We propose a consistent one-shot tuning method to estimate $star$ without grid search, sample splitting, or refitting.
  arXiv  Detail & Related papers  (2026-02-19T17:21:15Z)
• Latent Generative Solvers for Generalizable Long-Term Physics Simulation [12.894423121609526]
  Latent Generative Solvers (LGS) is a framework that maps diverse PDE states into a shared latent physics space with a pretrained VAE.<n>Our key mechanism is an uncertainty knob that perturbs latent inputs during training and inference, teaching the solver to correct off-manifold rollout drift.<n>LGS matches strong deterministic neural-operator baselines on short horizons while substantially reducing rollout drift on long horizons.
  arXiv  Detail & Related papers  (2026-02-11T15:34:52Z)
• Constraint-Aware Discrete-Time PID Gain Optimization for Robotic Joint Control Under Actuator Saturation [18.71390061417015]
  Practical loops deviate from continuous-time theory due to discrete-time execution, actuator saturation, and small delays and measurement imperfections.<n>We present an implementation-aware analysis and tuning workflow for saturated discrete-time joint control.
  arXiv  Detail & Related papers  (2026-01-26T16:11:05Z)
• SADA: Stability-guided Adaptive Diffusion Acceleration [24.250318487331228]
  Diffusion models have achieved remarkable success in generative tasks but suffer from high computational costs.<n>Existing training-free acceleration strategies that reduce per-step computation cost, while effectively reducing sampling time, demonstrate low faithfulness.<n>We propose Stability-guided Adaptive Diffusion Acceleration (SADA), a novel paradigm that accelerates sampling of ODE-based generative models.
  arXiv  Detail & Related papers  (2025-07-23T02:15:45Z)
• Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum [78.27945336558987]
  Decentralized server (DFL) eliminates reliance on client-client architecture.<n>Non-smooth regularization is often incorporated into machine learning tasks.<n>We propose a novel novel DNCFL algorithm to solve these problems.
  arXiv  Detail & Related papers  (2025-04-17T08:32:25Z)
• COmoving Computer Acceleration (COCA): $N$-body simulations in an emulated frame of reference [0.0]
  We introduce COmoving Computer Acceleration (COCA), a hybrid framework interfacing machine learning and $N$-body simulations.<n>The correct physical equations of motion are solved in an emulated frame of reference, so that any emulation error is corrected by design.<n>COCA efficiently reduces emulation errors in particle trajectories, requiring far fewer force evaluations than running the corresponding simulation without ML.
  arXiv  Detail & Related papers  (2024-09-03T17:27:12Z)
• Momentum Accelerates the Convergence of Stochastic AUPRC Maximization [80.8226518642952]
  We study optimization of areas under precision-recall curves (AUPRC), which is widely used for imbalanced tasks. We develop novel momentum methods with a better iteration of $O (1/epsilon4)$ for finding an $epsilon$stationary solution. We also design a novel family of adaptive methods with the same complexity of $O (1/epsilon4)$, which enjoy faster convergence in practice.
  arXiv  Detail & Related papers  (2021-07-02T16:21:52Z)
• Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT [106.83952081124195]
  Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes. We propose an adaptive ADMM (asI-ADMM) algorithm and apply it to decentralized RL with edge-computing-empowered IIoT networks. Experiment results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability, and can well adapt to complex IoT environments.
  arXiv  Detail & Related papers  (2021-06-30T16:49:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.