AI agents are increasingly deployed in real-world applications, including systems such as Manus, OpenClaw, and coding agents. Existing research has primarily focused on emph{server-side} efficiency, ...
Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...
PyBADS is a Python implementation of the Bayesian Adaptive Direct Search (BADS) algorithm for solving difficult and mildly expensive optimization problems, originally implemented in MATLAB. BADS has ...
In chemical research and industrial production, experimental optimization has long been a time-consuming and labor-intensive challenge. From adjusting reaction temperature and pressure to improve ...
Accurate disaster prediction combined with reliable uncertainty quantification is crucial for timely and effective decision-making in emergency management. However, traditional deep learning methods ...
Optimizing operational conditions for complex biological systems used in life sciences research and biotechnology is an arduous task. Here, we apply a Bayesian Optimization-based iterative framework ...
Conin supports constrained inference and learning for hidden Markov models, Bayesian networks, dynamic Bayesian networks and Markov networks. Conin interfaces with the pgmpy python library for the ...
ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...
A research group has developed SPACIER, an advanced polymer material design tool that integrates machine learning with molecular simulations. As a proof of concept, the group successfully synthesized ...
Abstract: Reinforcement learning (RL) has revolutionized decision-making across a wide range of domains over the past few decades. Yet, deploying RL policies in real-world scenarios presents the ...
The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are ...
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