Abstract: An analog circuit for solving optimization algorithms comprises three voltage controlled current sources and three capacitors, operatively coupled in parallel to the three voltage controlled current sources, respectively. The circuit further comprises a first inductor, operatively coupled in series between a first pair of the capacitors and the voltage controller current sources and a second pair of the capacitors and the voltage controller current sources. The circuit further comprises a second inductor, operatively coupled in series between the second pair of the capacitors and the voltage controller current sources and a third pair of the capacitors and the voltage controller current sources.

Abstract: One embodiment provides a method and a system for reconstructing symbols transmitted over a high frequency (HF) communication channel. During operation, the system can receive, at a receiver, a radio frequency (RF) signal carrying an input data frame and transmitted over the HF communication channel. The input data frame includes a number of known symbols followed by a number of unknown symbols. The system can determine a set of channel parameters associated with the HF communication channel based on the received RF signal and the known symbols and reconstruct, using a machine-learning technique, the unknown symbols based on the determined channel parameters and the received RF signal.

Abstract: A component library having a plurality of design components is received. Designs are predicted using the plurality of components using a machine learning model. The predicted designs comprise a subset of all possible designs using the plurality of components. A set of design criteria is received. At least one design solution is generated based on the set of design criteria and the predicted designs.

Abstract: System and method that allow to control density distributions of multiple particles (micro-or-nano-sized objects) to desired positions are described. A kernel density estimation (KDE) is used as a proxy for the initial particle density distribution and an optimal control problem is defined and solved using this approximation. A sequence of electrode electric potentials is computed so that the initial particle distribution is shaped into a target distribution after applying this sequence over time. The optimal control cost function is defined in terms of an L2 metric, with the L2 function that is used to compute the error between the particle density at the end of a time horizon and a target density. The KDE depends on the predicted trajectories of a set of particles, where the trajectory of a single particle is determined by a lumped, 2D, capacitive-based, nonlinear model describing the particle's motion.

Abstract: One embodiment provides a method and a system for diagnosing a digital circuit. During operation, the system can obtain a design of the digital circuit, generate a design of a diagnostic circuit by augmenting the design of the digital circuit based on a number of fault-emulating subcircuits, and convert the design of the diagnostic circuit to a design of a quantum oracle circuit. The system can further construct a quantum diagnostic circuit based on the design of the quantum oracle circuit and observe states of the quantum diagnostic circuit to determine probability distributions of one or more faults in the digital circuit.

Abstract: System and method that to shape micro-object density distribution (how densely the micro-objects are assembled in particular spatial regions) are provided. A high speed camera tracks existing object density distribution. An array of photo-transistor-controlled electrodes is used to generate a dynamic potential energy landscape for manipulating objects with both DEP and EP forces, and a video projector is used actuate the array. One or more computing devices are used to: process images captured by the camera to estimate existing density distribution of objects; receive a desired density distribution of micro-objects; define a model describing a variation of micro-object density over time due to capacitance-based interactions; generate a sequence of electrode potential that when generated would minimize error between the existing density distribution and a desired density distribution; and use the sequences of electrode potentials to actuate the electrodes.

Abstract: One embodiment provides a method and a system for diagnosing faults in a physical system. During operation, the system can create a fault-augmented model of the physical system by considering various potential faults, and it can generate a machine-learning model to predict an operation mode of the physical system using the outputs of the physical system. A respective operation mode corresponds to normal operation or a potential fault in the physical system. The system can generate a plurality of training samples based on the fault-augmented model, use the training samples to train the machine-learning model to learn a sequence of inputs and model parameters that minimizes an uncertainty of the predicted operation mode, and then apply the learned sequence of inputs and the trained machine-learning model on the physical system to determine the operation mode of the physical system.

Abstract: A classification-based diagnosis for detecting and predicting faults in physical system (e.g. an electronic circuit or rail switch) is disclosed. Some embodiments make use of partial system model information (e.g., system topology, components behavior) to simplify the classifier complexity (e.g., reduce the number of parameters). Some embodiments of the method use a Bayesian approach to derive a classifier structure.

Abstract: The techniques disclosed herein help designers find interesting designs for small electrical, mechanical, and/or hydraulic mechanisms by exhaustively enumerating the design space given a library of components and a maximum number of components allowed per design. Some embodiments work by creating a design space grammar of designs, solving the equations associated with parts of the grammar, and putting the solutions into equivalence classes. This dramatically reduces the number of designs that have to be evaluated to see if they satisfy the design criteria. The result is often a small number of base designs that show the range of possible solutions to the design problem.

Abstract: System and method that allow utilize machine learning algorithms to move a micro-object to a desired position are described. A sensor such as a high speed camera or capacitive sensing, tracks the locations of the objects. A dynamic potential energy landscape for manipulating objects is generated by controlling each of the electrodes in an array of electrodes. One or more computing devices are used to: estimate an initial position of a micro-object using the sensor; generate a continuous representation of a dynamic model for movement of the micro-object due to electrode potentials generated by at least some of the electrodes and use automatic differentiation and Gauss quadrature rules on the dynamic model to derive optimum potentials to be generated by the electrodes to move the micro-object to the desired position; and map the calculated optimized electrode potentials to the array to activate the electrodes.

Abstract: One embodiment provides a method and a system for computing diagnoses for a physical system. During operation, the system can obtain a design of the physical system, generate a design of a diagnostic system by augmenting the design of the physical system based on a number of fault-emulating subsystems, and convert the design of the diagnostic system into a polynomial formula comprising a plurality of variables. The plurality of variables can include inputs and outputs of the original physical system and a number of ancillary variables. The system can further embed the polynomial formula on a hardware-based solver configured to perform optimization using the polynomial formula as an objective function to obtain a diagnostic vector used for explaining faults in the physical system.

Abstract: The techniques discussed herein generally relate to a method and system for qualitative modeling of and reasoning about the behavior of spatio-temporal physical systems. In some embodiments, qualitative representations based on Tonti diagrams are used to describe lumped or distributed parameter systems. Using a topological structure of the physical system, some embodiments generate qualitative governing equations as symbolic constraints on qualitative state variables. The qualitative constraints may be used to produce a qualitative simulation of the physical system. The qualitative simulation may be used to guide conceptual design iterations with given design criteria, or for instantiation of quantitative or hybrid (qualitative and quantitative) models and simulations.

Abstract: One embodiment provides a method and a system for automated design of a physical system. During operation, the design system obtains qualitative and quantitative design requirements associated with the physical system and inputs the qualitative design requirements to a trained machine-learning model to generate a topology of the physical system. The topology specifies a number of components and connections among the components. The design system then determines parameters of the components based on the quantitative design requirements.

Abstract: A nonlinear dynamic control system is defined by a set of equations that include a state vector and one or more control inputs. Via a machine learning method, a sub-optimal controller is derived that stabilizes the nonlinear dynamic control system at an equilibrium point. The sub-optimal controller is retrained to be used as a stabilizing controller for the nonlinear dynamic control system under general operating conditions.

Abstract: A transform memory controller is described herein wherein the transform memory controller comprises logic elements configured to perform desired transform operations on data that flows to-and-from conventional computer memory elements. The transform operations are configured to perform operations on such data without the need for such data to travel to-and-from the conventional computer memory element via the processor (e.g., Central Processing Unit (CPU)) of the computer system. Several desirable transform operations are herein disclosed.

Abstract: Methods and systems useful for artificial intelligence-assisted generation of viable hypotheses may have (1) an ability to rapidly enumerate and test a diverse set of mathematically sound and parsimonious physical hypotheses, starting from a few basic assumptions on the embedding spacetime topology; (2) a distinction between non-negotiable mathematical truism (e.g., conservation laws or symmetries), that are directly implied by properties of spacetime, and phenomenological relations (e.g., constitutive laws), whose characterization relies indisputably on empirical observation, justifying targeted use of data-driven methods (e.g., machine learning (ML) or polynomial regression); and (3) a “simple-first” strategy (following Occam’s razor) to search for new hypotheses by incrementally introducing latent variables that are expected to exist based on topological foundations of physics.

Abstract: One embodiment provides a method and a system for diagnosing faults in a physical system. During operation, the system can obtain a model of the physical system comprising a plurality of components and can perform a structural analysis on the model to decompose the model into multiple independent subsystem models. A subsystem model corresponds to a subsystem comprising a subset of the plurality of components. The system can then perform, in parallel, a fault-diagnosis operation on each subsystem based on the corresponding subsystem model and can generate a diagnostic output indicating one or more components within the physical system being faulty based on outcomes of the fault-diagnosis operation on each subsystem.

Abstract: A method includes obtaining a binary code of a controller. The method also includes decompiling the binary code of the controller to generate a source code. The method further includes generating one or more abstract syntax trees based on the source code. The method further includes generating an interpretable model based on the one or more abstract syntax trees. The interpretable model is interpretable by subject matter experts.

Abstract: Embodiments described herein provide a parameter manager for determining system parameters. During operation, the parameter manager can determine a set of parameters for generating a distribution of feasible parameters needed for designing a system. The parameter manager can map, using a hybrid generator of an artificial intelligence (AI) model, input samples from a predetermined distribution to a set of parameters. The parameter manager can then generate, using the mapping, a set of parameter samples corresponding to the set of parameters from the predetermined distribution. The parameter manager can also generate, using a physical model of the system in the hybrid generator, a set of outputs of the system induced by the set of parameter samples. The parameter manager can iteratively update the hybrid generator until the set of outputs follow an expected output of the system, thereby ensuring feasibility for the set of parameter samples.

Abstract: The following relates generally to defense mechanisms and security systems. Broadly, systems and methods are disclosed that detect an anomaly in an Embedded Mission Specific Device (EMSD). Disclosed approaches include a meta-material antenna configured to receive a radio frequency signal from the EMSD, and a central reader configured to receive a signal from the meta-material antenna. The central reader may be configured to: build a finite state machine model of the EMSD based on the signal received from the meta-material antenna; and detect if an anomaly exists in the EMSD based on the built finite state machine model.