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1. Multi-Stability Property of Magneto-Kresling Truss Structures

   https://doi.org/10.1115/1.4051705  

   Yang, Xinyan; Keten, Sinan (September 2021, Journal of Applied Mechanics)

   Abstract The Kresling truss structure, derived from Kresling origami, has been widely studied for its bi-stability and various other properties that are useful for diverse engineering applications. The stable states of Kresling trusses are governed by their geometry and elastic response, which involves a limited design space that has been well explored in previous studies. In this work, we present a magneto-Kresling truss design that involves embedding nodal magnets in the structure, which results in a more complex energy landscape, and consequently, greater tunability under mechanical deformation. We explore this energy landscape first along the zero-torque folding path and then release the restraint on the path to explore the complete two-degree-of-freedom behavior for various structural geometries and magnet strengths. We show that the magnetic interaction could alter the potential energy landscape by either changing the stable configuration, adjusting the energy well depth, or both. Energy wells with different minima endow this magneto-elastic structure with an outstanding energy storage capacity. More interestingly, proper design of the magneto-Kresling truss system yields a tri-stable structure, which is not possible in the absence of magnets. We also demonstrate various loading paths that can induce desired conformational changes of the structure. The proposed magneto-Kresling truss design sets the stage for fabricating tunable, scalable magneto-elastic multi-stable systems that can be easily utilized for applications in energy harvesting, storage, vibration control, as well as active structures with shape-shifting capability. 

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2. Calibration of optical parametric amplifiers for frequency-domain ultrafast coherent multidimensional spectroscopic studies

   https://doi.org/10.1063/5.0271282  

   Kain, Schuyler; Thompson, Blaise J; Kohler, Daniel D; Wright, John C (July 2025, Review of Scientific Instruments)

   Frequency-domain ultrafast coherent multidimensional spectroscopy has made possible a family of fully coherent spectroscopies that can create and interrogate characteristic superpositions of the quantum-mechanical states of a system under investigation. Typical applications include the resolution of couplings and dynamics among multiple electronic states in atoms, molecules, and materials. These methods require scanning the wavelengths of multiple, ultrafast light sources—often optical parametric amplifiers (OPAs). Spectral calibration of the OPA output (a.k.a. wavelength-tuning) involves optimizing the OPA output intensity by adjusting the angles of its component nonlinear crystals and motorized delay stages. When the spectral range addressed in the experiment is large, optimization and control of the one or more OPAs become complex. This work describes an automated calibration strategy that measures the multidimensional configuration-space of a typical 800-nm OPA over all angular and delay degrees-of-freedom in order to create a global tuning curve that spans its dynamic spectral range with optimal power and smooth interpolation. To accomplish this task, the optimization assesses the wavelength-dependent variations to the temporal and spatial characteristics of the OPA output caused by material dispersion so that compensations may be applied during a wavelength scan. 

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3. Expedited Learning in MDPs with Side Information

   https://doi.org/10.1109/cdc.2018.8619134  

   Ornik, Melkior; Fu, Jie; Lauffer, Niklas T.; Perera, W. K.; Alshiekh, Mohammed; Ono, Masahiro; Topcu, Ufuk (December 2018, IEEE Conference on Decision and Control)

   Standard methods for synthesis of control policies in Markov decision processes with unknown transition probabilities largely rely on a combination of exploration and exploitation. While these methods often offer theoretical guarantees on system performance, the number of time steps and samples needed to initially explore the environment before synthesizing a well-performing control policy is impractically large. This paper partially alleviates such a burden by incorporating a priori existing knowledge into learning, when such knowledge is available. Based on prior information about bounds on the differences between the transition probabilities at different states, we propose a learning approach where the transition probabilities at a given state are not only learned from outcomes of repeatedly performing a certain action at that state, but also from outcomes of performing actions at states that are known to have similar transition probabilities. Since the directly obtained information is more reliable at determining transition probabilities than second-hand information, i.e., information obtained from similar but potentially slightly different states, samples obtained indirectly are weighted with respect to the known bounds on the differences of transition probabilities. While the proposed strategy can naturally lead to errors in learned transition probabilities, we show that, by proper choice of the weights, such errors can be reduced, and the number of steps needed to form a near-optimal control policy in the Bayesian sense can be significantly decreased. 

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4. Manipulating deformable objects by interleaving prediction, planning, and control

   https://doi.org/10.1177/0278364920918299  

   McConachie, Dale; Dobson, Andrew; Ruan, Mengyao; Berenson, Dmitry (July 2020, The International Journal of Robotics Research)

   null (Ed.)

   We present a framework for deformable object manipulation that interleaves planning and control, enabling complex manipulation tasks without relying on high-fidelity modeling or simulation. The key question we address is when should we use planning and when should we use control to achieve the task? Planners are designed to find paths through complex configuration spaces, but for highly underactuated systems, such as deformable objects, achieving a specific configuration is very difficult even with high-fidelity models. Conversely, controllers can be designed to achieve specific configurations, but they can be trapped in undesirable local minima owing to obstacles. Our approach consists of three components: (1) a global motion planner to generate gross motion of the deformable object; (2) a local controller for refinement of the configuration of the deformable object; and (3) a novel deadlock prediction algorithm to determine when to use planning versus control. By separating planning from control we are able to use different representations of the deformable object, reducing overall complexity and enabling efficient computation of motion. We provide a detailed proof of probabilistic completeness for our planner, which is valid despite the fact that our system is underactuated and we do not have a steering function. We then demonstrate that our framework is able to successfully perform several manipulation tasks with rope and cloth in simulation, which cannot be performed using either our controller or planner alone. These experiments suggest that our planner can generate paths efficiently, taking under a second on average to find a feasible path in three out of four scenarios. We also show that our framework is effective on a 16-degree-of-freedom physical robot, where reachability and dual-arm constraints make the planning more difficult. 

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5. From data to noise to data for mixing physics across temperatures with generative artificial intelligence

   https://doi.org/10.1073/pnas.2203656119  

   Wang, Yihang; Herron, Lukas; Tiwary, Pratyush (August 2022, Proceedings of the National Academy of Sciences)

   Using simulations or experiments performed at some set of temperatures to learn about the physics or chemistry at some other arbitrary temperature is a problem of immense practical and theoretical relevance. Here we develop a framework based on statistical mechanics and generative artificial intelligence that allows solving this problem. Specifically, we work with denoising diffusion probabilistic models and show how these models in combination with replica exchange molecular dynamics achieve superior sampling of the biomolecular energy landscape at temperatures that were never simulated without assuming any particular slow degrees of freedom. The key idea is to treat the temperature as a fluctuating random variable and not a control parameter as is usually done. This allows us to directly sample from the joint probability distribution in configuration and temperature space. The results here are demonstrated for a chirally symmetric peptide and single-strand RNA undergoing conformational transitions in all-atom water. We demonstrate how we can discover transition states and metastable states that were previously unseen at the temperature of interest and even bypass the need to perform further simulations for a wide range of temperatures. At the same time, any unphysical states are easily identifiable through very low Boltzmann weights. The procedure while shown here for a class of molecular simulations should be more generally applicable to mixing information across simulations and experiments with varying control parameters. 

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