President Trump on $1,000 World Cup ticket prices: 'I wouldn't pay it either, to be honest' Pirates vs. Diamondbacks betting preview targets the under as both offenses go cold in series Former LSU coach Brian Kelly uses AI to prepare for job interviews, proving he's just like the rest of us Newsom office source responds to planned protest against trans athlete at state playoff girls' track meet Framber Valdez gets what he deserves for punk move, suspended six games after drilling Boston's Trevor Story MLB's new automated strike zone has a hidden feature helping umpires become more accurate than ever'This can touch anyone': Gorman family speaks following loss of Sheridan'Project Freedom' could soon resume: Report Iranian people are not citizens, but'subjects' of the regime: Middle East expert Vice Admiral Robert Harward weighs in on restarting'Project Freedom' in Strait of Hormuz Largest teachers' union accused of antisemitism in federal civil rights complaint McEnany's URGENT plea: 'Be Spencer Pratt!' WHO doesn't expect large Hantavirus outbreak US blockade keeps stranglehold on Iran's economy The Panthers star told Pat McAfee the U.S. is heading to Switzerland to win, not for a vacation If anyone thought Team USA was satisfied with Olympic gold and ready to coast through the rest of the international hockey calendar, Matthew Tkachuk has a message. The Florida Panthers star joined The Pat McAfee Show on Thursday and discussed his plan to play for Team USA at the 2026 IIHF World Championship in Switzerland. USA Hockey's preliminary roster, announced May 7, includes Tkachuk for the first time, since the Panthers failed to reach the NHL playoffs this season. The tournament begins May 15 in Zurich and Fribourg, and the Americans are trying to win back-to-back gold medals at the event for the first time ever. Tkachuk made his mindset pretty clear.

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Science Space Solar System Mars For 6 days, NASA's Mars rover battled a rock More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The multi-day challenge took multiple attempts to fix. Breakthroughs, discoveries, and DIY tips sent six days a week. Curiosity got itself stuck between a rock and hard place last month, but NASA says there's no reason to fret about the intrepid Mars rover . On April 25, mission engineers were remotely piloting its robotic arm's rotary-percussive drill into a Martian rock nicknamed Atacama.

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We develop a physics-informed learning framework for energy-shaping control of port-Hamiltonian (pH) systems from trajectory data. The proposed approach co-learns a pH system model and an optimal energy-balancing passivity-based controller (EB-PBC) through alternating optimization with policy-aware data collection. At each iteration, the system model is refined using trajectory data collected under the current control policy, and the controller is re-optimized on the updated model. Both components are parameterized by neural networks that embed the pH dynamics and EB-PBC structure, ensuring interpretability in terms of energy interactions. The learned controller renders the closed-loop system inherently passive and provably stable, and exploits passive plant dynamics without canceling the natural potential. A dissipation regularization enforces strict energy decay during training, thereby enhancing robustness to sim-to-real gaps. The proposed framework is validated on state-regulation and swing-up tasks for planar and torsional pendulum systems.

Bayesian optimization is a sequential method for minimizing objective functions that are expensive to evaluate and about which few assumptions can be made. By using all gathered data to train a Gaussian process model for the function and adaptively employing a mixture of global exploration and local exploitation, this method has been used for optimization in many fields including machine learning, automotive engineering and reinforcement learning. However, the standard method suffers from two problems: 1) with cubic computational complexity in the training-set size it eventually becomes computationally infeasible to train the model, and 2) globally modeling the objective function is not necessarily optimal given the local nature of minimization. Using flexible and recursive binary partitioning of the search space, we adapt both the modeling and acquisitive aspects of standard Bayesian optimization to work harmoniously with the partitioning scheme, thereby ameliorating both standard shortcomings. We compare our method against a commonly used Bayesian optimization library on seven challenging test functions, ranging in dimensionality from $6$ to $124$, and show that our method achieves superior optimization performance in all tests. In addition our method has linear computational complexity.