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.

Abstract: Methods may comprise: identifying a fault indicator associated with a physical system; collecting first data related to a state of the physical system; applying a surrogate model to the first data to produce a plurality of potential fault modes; applying an optimization algorithm to the plurality of potential fault modes using a similarity metric to produce an input and a plurality of outputs, wherein each of the plurality of outputs corresponds to one of the plurality of potential fault modes, wherein the input provides differentiation between each of the plurality of outputs; applying the input to the physical system; collecting second data from physical system in response to applying the input; identifying a true mode of the physical system based on a comparison of the second data and the plurality of outputs; and diagnosing a fault of the physical system based on the true mode.

Abstract: Quantum field-programmable analog arrays (FPAAs) may be useful in solving differential equations. For example, a quantum FPAA may comprise: an array of computational analog blocks (CABs) configured to perform a mathematical operation; and an interconnection network connecting the CABs, the interconnection network comprising communication paths and switches. Said quantum FPAAs may be useful in integrated chips, computing systems, and related methods.

Abstract: A target system is coupled to a diagnosis engine that uses a lumped parameter model of the system for diagnosis. A proximity search in is performed in a computer-aided design model of the system to find groups of components that may be affected by resistive or parasitic interactions between the individual components in the groups. The lumped parameter model is augmented by adding elements that emulate the resistive or parasitic interactions between the individual components in the groups. The augmented lumped model is used by the diagnosis engine to perform diagnosis on the system.

Abstract: The disclosure following relates generally to complex simulations, and fault diagnosis. In some embodiments, a component that is causing a delayed simulation time of a system is determined. A component of reduced complexity is designed, and the component of reduced complexity is used to replace the original component in the system. Fault diagnosis may then be conducted using the updated system with the reduced complexity component, thus decreasing the time taken to diagnose the fault.

Abstract: One embodiment provides a method and a system for generating test vectors for testing a computational system. During operation, the system obtains a design of the computational system, the design comprising an original system. The system generates a design of a fault-augmented system block by adding a plurality of fault-emulating subsystems to the original system; generates a design of an equivalence-checking system based on the original system and the fault-augmented system block; encodes the design of the equivalence-checking system into a logic formula, with variables within the logic formula comprising inputs and outputs of the original system and inputs and outputs of the fault-augmented system block; and solves the logic formula to obtain a test vector used for testing at least one fault in the computational system.

Abstract: One embodiment provides a method and a system for generating test vectors for testing a computational system. During operation, the system obtains a design of the computational system, the design comprising an original system. The system generates a design of a fault-augmented system block by adding a plurality of fault-emulating subsystems to the original system; generates a design of an equivalence-checking system based on the original system and the fault-augmented system block; encodes the design of the equivalence-checking system into a logic formula, with variables within the logic formula comprising inputs and outputs of the original system and inputs and outputs of the fault-augmented system block; and solves the logic formula to obtain a test vector used for testing at least one fault in the computational system.

Abstract: A nonlimiting example method for converting implicit dynamic models into explicit dynamic models comprises: performing a block lower triangular (BLT) transformation on equations of an implicit dynamic differential algebraic equation (DAE) model to yield a BLT representation of the implicit dynamic DAE model; performing a nonlinear block extraction for a diagonal of the BLT representation of the implicit dynamic DAE model to yield one or more nonlinear blocks and one or more linear blocks; constructing a surrogate causal model for each of the one or more nonlinear blocks; training the surrogate causal model for each of the one or more nonlinear blocks; and constructing an explicit dynamic ordinary differential equation (ODE) model that corresponds to the implicit dynamic DAE model based on the linear blocks and the surrogate causal model. The explicit ODE model may be useful for controlling operations, diagnosing issues, and prognosticating conditions within a physical system.

Abstract: One embodiment of the present disclosure provides a system for determining a hybrid-manufacturing plan for manufacturing an object. During operation, the system can obtain a set of hybrid-manufacturing constraints for manufacturing the object. The set of hybrid-manufacturing constraints can include a set of primitives, a set of atoms, and an atom end-state vector. An atom can correspond to a unit of spatial volume of the object. A primitive can represent an additive or a subtractive manufacturing process corresponding to one or more atoms of the object. Next, the system can determine a plurality of feasible hybrid-manufacturing plans based on the set of hybrid-manufacturing constraints. Each feasible hybrid-manufacturing plan can represent an ordering of the set of primitives that satisfies the atom end-state vector. The system can then determine costs for manufacturing the object using the plurality feasible hybrid-manufacturing plans.

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: 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: 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: A computer is provided with a geometric representation of an as-designed part and a set of hybrid manufacturing capabilities. The computer computes a set of additive and subtractive manufacturing primitives from the provided set of hybrid manufacturing capabilities, and intersects the primitives to generate an atomic decomposition of space. The computer uses the atomic decomposition to generate a non-geometric representation of a space of manufacturable parts with hybrid manufacturing capabilities in at least one of symbolic, logical, and combinatorial forms. At least one of a necessary, sufficient, or necessary-and-sufficient condition for manufacturability is tested via examining the non-geometric representation for the existence of at least one feasible process plan whose outcome is an as-manufactured part that is interchangeable with the as-designed part.

Abstract: A system and method for determining vehicle component conditions is provided. A predictive model is built for a vehicle component and values are mapped for a feature of the vehicle component using the predictive model. A threshold is applied to the mapped values. An occurrence of a fault of the vehicle component is predicted when one or more of the mapped values exceeds the threshold and an extended optimal interval during which the fault is predicted to occur is identified.