Abstract: Embodiments described herein provide a supervisor for fault management at a production system. During operation, the supervisor can obtain a set of sensor readings and a state of the production system. A respective sensor reading is an output of a sensor in the production system. The supervisor can then determine, using an artificial intelligence (AI) model, whether the set of sensor readings accommodates a fault associated with a corresponding sensor. Subsequently, the supervisor can determine an action that mitigates an effect of the fault and modify the set of sensor readings based on the action. Here, the modified set of sensor readings is used by a controller that controls the production system.

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: Embodiments described herein provide a design architecture for co-designing a controller and a watermarking signal for a cyber-physical system. During operation, the architecture can determine, in conjunction with each other, respective values of a first set of parameters indicating operations of the controller and a second set of parameters representing the watermarking signal. Here, the watermarking signal is combinable with a control signal from the controller for monitoring an output signal of the cyber-physical system for detecting malicious data at different time instances. Subsequently, the architecture can determine a state manager for determining the states of the cyber-physical system from the monitored output signal based on the first and second sets of parameters. The architecture can also determine a detector capable of identifying presence of an attack from the states of the cyber-physical system at a plurality of time instances using the watermarking signal.

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: 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: Embodiments described herein provide a supervisor for fault management at a production system. During operation, the supervisor can obtain a set of sensor readings and a state of the production system. A respective sensor reading is an output of a sensor in the production system. The supervisor can then determine, using an artificial intelligence (AI) model, whether the set of sensor readings accommodates a fault associated with a corresponding sensor. Subsequently, the supervisor can determine an action that mitigates an effect of the fault and modify the set of sensor readings based on the action. Here, the modified set of sensor readings is used by a controller that controls the production system.

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: 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: 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: 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: 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: One embodiment can provide a system for determining a hybrid-manufacturing process plan for manufacturing a printed circuit board (PCB). During operation, the system can obtain a set of hybrid-manufacturing constraints. The set of hybrid-manufacturing constraints can include a set of primitives, a set of atoms, and an atom end-state vector. A primitive can represent an additive or a subtractive manufacturing process corresponding to one or more atoms of the PCB. An atom can correspond to a unit of spatial volume of the PCB. 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 satisfying the atom end-state vector. The system can determine costs for manufacturing the PCB using the plurality of feasible hybrid-manufacturing plans. The system can determine, based on the costs, an optimized hybrid-manufacturing plan for manufacturing the PCB.

Abstract: A method and system for automated design of a physical system are provided. During operation, the system obtains a component library comprising a plurality of physical components, receives design requirements of the physical system, and constructs an initial system model based on physical components in the component library and the design requirements. The system topology associated with the initial system model can include a large number of links that are sufficiently coupled to one another, and a respective link comprises one or more physical components. The system further performs an optimization operation comprising a plurality of iterations, with the system topology being updated at each iteration. Updating the system topology includes removing links and components from the system topology. The system then generates a final system model based on an outcome of the optimization operation and outputs a design solution of the physical system according to the final system model.

Abstract: One embodiment of the present disclosure provides a system for determining a hybrid-manufacturing plan for manufacturing an integrated circuit (IC). During operation, the system can obtain a set of hybrid-manufacturing constraints for manufacturing the IC. 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 IC. A primitive can represent an additive, subtractive, or a mixed manufacturing process corresponding to one or more atoms of the IC. Next, the system can determine a plurality of feasible hybrid-manufacturing plans based on the set of 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 IC using the plurality feasible hybrid-manufacturing plans.

Abstract: One embodiment can provide a method and a system for diagnosing faults in a physical system. During operation, the system obtains a time-domain model of the physical system and converts the time-domain model to the frequency domain to obtain a frequency-domain model of the physical system. The time-domain model can include one or more model parameters having known values. The system also obtains time-domain input and output signals and converts the time-domain input and output signals to the frequency domain to obtain frequency-domain input and output signals. The system identifies at least one model parameter having an expected value that is different from a known value of the at least one model parameter based on the frequency-domain model and the frequency-domain input and output signals, and generates a diagnostic output indicating at least one component within the physical system being faulty based on the identified at least one model parameter.