Model predictive control (MPC), widely used for real-time control of complex dynamical systems, operates by repeatedly solving an optimization problem over a receding time horizon. Its success hinges on dynamical models that are accurate yet efficient enough for rapid online computation. Frequently, the governing models of complex systems are either unknown or computationally inefficient, forcing MPC to rely on data-driven surrogate models. Echo state networks (ESNs), a class of recurrent neural networks trained through computationally efficient ridge regression, are well-suited for this role and have demonstrated strong forecasting capabilities in chaotic dynamical systems. Their architecture naturally supports rapid training and flexible adaptation to varying control inputs. In this work, we demonstrate that ESNs serve as effective data-driven surrogates for system dynamics under diverse control scenarios, outperforming competing architectures such as long short-term memory (LSTM) networks. On challenging control benchmarks, including the Lorenz system with control and fluid flow past a cylinder, MPC with ESN surrogates consistently achieves the control objective, whereas the next-best considered architecture, LSTM-based MPC, frequently fails. Even in cases where LSTM-based MPC succeeds, ESN-based MPC reduces average control cost by up to 10% and decreases variability by as much as 85%. Beyond performance, ESNs are significantly more sample-efficient and train over an order of magnitude faster than LSTMs. These results establish ESNs as accurate, efficient architectures for scalable data-driven MPC in complex systems with limited training data and unknown dynamics.

Machine learning (ML) is transforming modeling and control in the physical, engineering, and biological sciences. However, rapid development has outpaced the creation of standardized, objective benchmarks—leading to weak baselines, reporting bias, and inconsistent evaluations across methods. This undermines reproducibility, misguides resource allocation, and obscures scientific progress. To address this, we propose a Common Task Framework (CTF) for scientific machine learning. The CTF features a curated set of datasets and task-specific metrics spanning forecasting, state reconstruction, and generalization under realistic constraints, including noise and limited data. Inspired by the success of CTFs in fields like natural language processing and computer vision, our framework provides a structured, rigorous foundation for head-to-head evaluation of diverse algorithms. As a first step, we benchmark methods on two canonical nonlinear systems: Kuramoto-Sivashinsky and Lorenz. These results illustrate the utility of the CTF in revealing method strengths, limitations, and suitability for specific classes of problems and diverse objectives. Next, we are launching a competition around a global real world sea surface temperature dataset with a true holdout dataset to foster community engagement. Our long-term vision is to replace ad hoc comparisons with standardized evaluations on hidden test sets that raise the bar for rigor and reproducibility in scientific ML.