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We consider the online restless bandits with average-reward and multiple actions, where the state of each arm evolves according to a Markov decision process (MDP), and the reward of pulling an arm depends on both the current state of the corresponding MDP and the action taken. Since finding the optimal control is typically intractable for restless bandits, existing learning algorithms are often computationally expensive or with a regret bound that is exponential in the number of arms and states. In this paper, we advocate \textit{index-aware reinforcement learning} (RL) solutions to design RL algorithms operating on a much smaller dimensional subspace by exploiting the inherent structure in restless bandits. Specifically, we first propose novel index policies to address dimensionality concerns, which are provably optimal. We then leverage the indices to develop two low-complexity index-aware RL algorithms, namely, (i) GM-R2MAB, which has access to a generative model; and (ii) UC-R2MAB, which learns the model using an upper confidence style online exploitation method. We prove that both algorithms achieve a sub-linear regret that is only polynomial in the number of arms and states. A key differentiator between our algorithms and existing ones stems from the fact that our RL algorithms contain a novel exploitation that leverages our proposed provably optimal index policies for decision-makings.
Agentic systems now address tasks across theoretical, phenomenological, and experimental high energy physics (HEP), but their scientific capabilities remain difficult to reuse across different large language models, providers, and harnesses. We argue that stable parts of these workflows should be promoted into versioned scientific operations and exposed through common protocols. Existing general-purpose harnesses can then be specialized for HEP through task-specific sets of tools and skills, while community-maintained registries would make these capabilities discoverable and citable. We identify mismatches in conventions, assumptions, and domains of validity among independently developed operations as a potential obstacle to their composition, and discuss machine-readable scientific contracts as one possible solution. These design principles and evaluation guidelines provide a near-term path toward a portable and community-maintained agentic harness for HEP.
I. Designs are constructed incrementally to meet requirements and solve problems: a) Requirements types: objectives, scenarios, constraints, ilities. etc. b) Problem/issue types: risk/safety, cost/difficulty, interaction, conflict, etc. II. Capture requirements, problems and solutions: a) Collect design and analysis products and make them accessible for integration and analysis; b) Link changes in design requirements, problems and solutions; and c) Harvest design data for design models and choice structures. III. System designs are constructed by multiple groups designing interacting subsystems a) Diverse problems, choice criteria, analysis methods and point solutions. IV. Support integration and global analysis of repercussions: a) System implications of point solutions; b) Broad analysis of interactions beyond totals of mass, cost, etc.
The application of conventional optimization schemes to aerodynamic design problems leads to inner-outer iterative procedures that are very costly. An alternative approach is presented based on the idea of updating the flow variable iterative solutions and the design parameter iterative solutions simultaneously. Two schemes based on this idea are applied to problems of correcting wind tunnel wall interference and optimizing advanced propeller designs. The first of these schemes is applicable to a limited class of two-design-parameter problems with an equality constraint. It requires the computation of a single flow solution. The second scheme is suitable for application to general aerodynamic problems. It requires the computation of several flow solutions in parallel. In both schemes, the design parameters are updated as the iterative flow solutions evolve. Computations are performed to test the schemes' efficiency, accuracy, and sensitivity to variations in the computational parameters.
Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. Yet, the intricate connectivity and complexity of biosystems pose a major hurdle in designing biosystems with desired features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find non-obvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocess. Lastly, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.
This analysis pertains to the applicability of optimal sensitivity information to aerospace vehicle design. An optimal sensitivity (or post-optimality) analysis refers to computations performed once the initial optimization problem is solved. These computations may be used to characterize the design space about the present solution and infer changes in this solution as a result of constraint or parameter variations, without reoptimizing the entire system. The present analysis demonstrates that post-optimality information generated through first-order computations can be used to accurately predict the effect of constraint and parameter perturbations on the optimal solution. This assessment is based on the solution of an aircraft design problem in which the post-optimality estimates are shown to be within a few percent of the true solution over the practical range of constraint and parameter variations. Through solution of a reusable, single-stage-to-orbit, launch vehicle design problem, this optimal sensitivity information is also shown to improve the efficiency of the design process, For a hierarchically decomposed problem, this computational efficiency is realized by estimating the main-problem objective gradient through optimal sep&ivity calculations, By reducing the need for finite differentiation of a re-optimized subproblem, a significant decrease in the number of objective function evaluations required to reach the optimal solution is obtained.
In the design of real-time systems, tasks are often assigned priorities. Preemptive priority driven schedulers are used to schedule tasks to meet the timing requirements. Priority inversion is the term used to describe the situation when a higher priority task's execution is delayed by lower priority tasks. Priority inversion can occur when there is contention for resources among tasks of different priorities. The duration of priority inversion could be long enough to cause tasks to miss their dead lines. Priority inversion cannot be completely eliminated. However, it is important to identify sources of priority inversion and minimize the duration of priority inversion. In this paper, a comprehensive review of the problem of and solutions to unbounded priority inversion is presented.
Idaho National Labs serves as the spearhead for many innovative energy solutions to the world's energy crisis. One such solution is the INL's Microreactor which is designed to deploy to extreme/remote environments where other sources of power are either unavailable or unreliable. In order to best design these energy solutions for their operational environments, it is crucial to understand how the design, components, and materials will respond to the environmental conditions. One key material in these innovative designs is a nuclear-grade graphite known as PCEA. This study examines the behavior of PCEA graphite under dynamic loading, similar to that which may occur in extreme environments. The objective is to characterize the dynamic behavior and produce an accurate, reliable constitutive model suitable for use in simulation tools such as INL's MOOSE. Graphite specimens were tested using a Split Hopkinson Pressure Bar (SHPB) to administer the dynamic compressive load. The SHPB was charged at various pressures to produce a range of strain rates on the material in compression. Data was acquired via strain gauges on the SHPB setup, from which stress, strain, and time data were collected. Analysis revealed the stress-strain behavior of the material as well as insights into the material behavior's relationship to strain rate. Further work must continue to characterize the various other dynamic behaviors of the material which will combine to create a substantially trustworthy constitutive model for this grade of nuclear-grade graphite. Ultimately, this will allow for realistic simulation of the material in reactor designs, allowing for prediction of design weaknesses and leading to improved designs for increased resilience, security, and reliability.
The aero design of an inward pumping spiral groove face seal using an in-house spread sheet was compared with predictions from the NASA code SPIRALG. The high pressure compressor exit of an aero gas turbine was chosen as the location for the candidate seal. This is a challenging environment as rotational velocity, pressure drop, and temperature are high. This presentation compares the resulting lift forces, leakages, and friction loss for various ride heights. Within practical ranges of ride height, the lift force predictions agreed well. However, both leakage and friction loss predictions were significantly different.
For the design and the construction of airplanes the control is of special significance, not only with regard to the flight mechhnical properties but also for the proportional arrangement of wing unit, fuselage, and tail unit. whereas these problems may be regarded as solved for direct control of airplane motions, that is, for immediate operation of the control surfaces, they are not clarified as to oscimtions, stability, and stress phenomena occurring in flight motions with Indirect control, ss realized for instance in tab control. Its modus operandi is based on the activation of a tab hinged to the trailing edge & the main control surface. Due to lift and drag variations, mcments originate about the axis of rotation of the main contnol surface which cause an up-or--down floating of the main control surface and thus a change in the direction of the airplane. Since this tab control means flying with free control surface , the treatment of this problem should provide the basis on which to judge stability, oscilhtton, and stress data.The present report is to represent a contribution toward the clarification of the problems arising and, to treat the longitudinal motion of an airplane.
Crystal growth has been of interest to physicists and engineers for a long time because of their unique properties. Single crystals are utilized in such diverse applications as pharmaceuticals, computers, infrared detectors, frequency measurements, piezoelectric devices, a variety of high-technology devices, and sensors. Solution crystal growth is one of the important techniques to grow a variety of crystals when the material decomposes at the melting point and a suitable solvent is available to make a saturated solution at a desired temperature. In this Technical Memorandum (TM) an attempt is made to give the fundamentals of growing crystals from solution including improved designs of various crystallizers. Since the same solution crystal growth technique could not be used in microgravity, the authors proposed a new cooled-sting technique to grow crystals in space. The authors experience from conducting two Space Shuttle solution crystal growth experiments are also detailed in this TM and the complexity of solution growth experiments to grow crystals in space are also discussed. These happen to be some of the early experiments performed in space, and various lessons learned are described. A brief discussion of protein crystal growth that shares basic principles of the solution growth technique is given, along with some flight hardware information for growth in microgravity.
The growth of crystals has been of interest to physicists and engineers for a long time because of their unique properties. Single crystals are utilized in such diverse applications as pharmaceuticals, computers, infrared detectors, frequency measurements, piezoelectric devices, a variety of high technology devices and sensors. Solution crystal growth is one of the important techniques to grow a variety of crystals when the material decomposes at the melting point and a suitable solvent is available to make a saturated solution at a desired temperature. In this chapter an attempt is made to give some fundamentals of growing crystals from solution including improved designs of various crystallizers. Since the same solution crystal growth technique could not be used in microgravity, authors had proposed a new cooled sting technique to grow crystals in space. Authors? experiences of conducting two space shuttle experiments relating to solution crystal growth are also detailed in this work. The complexity of these solution growth experiments to grow crystals in space are discussed. These happen to be some of the early experiments performed in space, and various lessons learned are described. A brief discussion of protein crystal growth that also shares basic principles of solution growth technique is given along with some flight hardware information for its growth in microgravity.
An all-at-once reduced Hessian Successive Quadratic Programming (SQP) scheme has been shown to be efficient for solving aerodynamic design optimization problems with a moderate number of design variables. This paper extends this scheme to allow solution refining. In particular, we introduce a reduced Hessian refining technique that is critical for making a smooth transition of the Hessian information from coarse grids to fine grids. Test results on a nozzle design using quasi-one-dimensional Euler equations show that through solution refining the efficiency and the robustness of the all-at-once reduced Hessian SQP scheme are significantly improved.
An all-at-once reduced Hessian Successive Quadratic Programming (SQP) scheme has been shown to be efficient for solving aerodynamic design optimization problems with a moderate number of design variables. This paper extends this scheme to allow solution refining. In particular, we introduce a reduced Hessian refining technique that is critical for making a smooth transition of the Hessian information from coarse grids to fine grids. Test results on a nozzle design using quasi-one-dimensional Euler equations show that through solution refining the efficiency and the robustness of the all-at-once reduced Hessian SQP scheme are significantly improved.
Control over spatial concentration fields represents a fundamental challenge in designing synthetic biological systems and programmable soft materials. While nature creates morphogen gradients that orchestrate complex developmental processes, synthetic approaches have largely relied on empirical optimization and computationally intensive simulations. Here, we present an analytical framework for steady-state concentration fields generated by finite-sized localized sources in diffusion–degradation systems and derive closed-form solutions for one-, two-, and three-dimensional geometries. By expressing these solutions in dimensionless form, we show that gradient steepness and spatial structure are organized by the Thiele modulus, which captures the competition between diffusion and degradation length scales. The analysis reveals distinct design regimes: in degradation-dominated systems, gradient shape is governed by exponential decay and becomes dimension-independent, whereas in diffusion-dominated systems, gradient magnitude and extent follow dimension-dependent power-law scaling. Building on these results, we introduce a quantitative design strategy that uses threshold-based criteria to program concentration ranges by tuning physically accessible parameters, most directly the production rate, while holding transport and degradation properties fixed. Comparisons with numerical solutions and reported experimental systems demonstrate consistency with the predicted scaling behavior. Together, this work provides a generalizable and physically transparent framework for designing steady-state concentration fields in synthetic biological and soft matter systems, enabling predictive control of gradient-mediated organization without reliance on extensive numerical optimization.
This report examines the design of a long range cargo transport to attempt to reduce ton-mile shipping costs and to stimulate the air cargo market. This design effort involves the usual issues but must also include consideration of: airport terminal facilities; cargo loading and unloading; and defeating the 'square-cube' law to design large structures. This report reviews the long range transport design problem and several solutions developed by senior student design teams at Purdue University. The results show that it will be difficult to build large transports unless the infrastructure is changed and unless the basic form of the airplane changes so that aerodynamic and structural efficiencies are employed.
The success of a future clean hydrogen infrastructure will depend on technology performance, operating conditions, and system configuration, which will be integrated to meet specific end-use requirements. We introduce a Systems-to-Atoms framework to cosimulate material, device, and system design for clean hydrogen storage and transport. Here we present a demonstration scenario in which hydrogen is distributed using a liquid organic hydrogen carrier to 700 bar refueling stations. Cross-scale analysis on hydrogen release at the refueling station reveals that while a high reaction pressure (up to 100 bar) may not enhance the catalyst performance, it can lead to system-level cost savings (up to 5% of total refueling station cost) due to savings in downstream gas compression. Further, a typically low-performing dehydrogenation catalyst material (e.g., copper) may be preferred over a high-performing catalyst (e.g., palladium) at different operating conditions when considering overall system affordability and supply chain resilience.