Systems and techniques that facilitate Hamiltonian simulation based on simultaneous-diagonalization are provided. In various embodiments, a partition component can partition one or more Pauli operators of a Hamiltonian into one or more subsets of commuting Pauli operators. In various embodiments, a diagonalization component can generate one or more simultaneous-diagonalization circuits corresponding to the one or more subsets. In various aspects, a one of the one or more simultaneous-diagonalization circuits can diagonalize the commuting Pauli operators in a corresponding one of the one or more subsets. In various embodiments, an exponentiation component can generate one or more exponentiation circuits corresponding to the one or more subsets. In various aspects, a one of the one or more exponentiation circuits can exponentiate the simultaneously diagonalized commuting Pauli operators in a corresponding one of the one or more subsets. In various embodiments, a simulation component can concatenate the one or more simultaneous-diagonalization circuits, the one or more exponentiation circuits, and one or more adjoints of the one or more simultaneous-diagonalization circuits of the one or more subsets to simulate a time evolution of the Hamiltonian.

Various examples are provided related to automated chip design, such as a pareto-optimization framework for automated network-on-chip design. In one example, a method for network-on-chip (NoC) design includes determining network performance for a defined NoC configuration comprising a plurality of n routers interconnected through a plurality of intermediate links; comparing the network performance of the defined NoC configuration to at least one performance objective; and determining, in response to the comparison, a revised NoC configuration based upon iterative optimization of the at least one performance objective through adjustment of link allocation between the plurality of n routers. In another example, a method comprises determining a revised NoC configuration based upon iterative optimization of at least one performance objective through adjustment of a first number of routers to obtain a second number of routers and through adjustment of link allocation between the second number of routers.

A method, computer program product, and computing system are provided for receiving reservoir data associated with the reservoir. A simulation may be performed on the reservoir data to generate simulated reservoir data. A subset of realizations including a minimal number of realizations from a plurality of realizations may be determined based upon, at least in part, one or more statistical moments of the simulated reservoir data. An optimized reservoir model associated with an objective may be generated based upon, at least in part, the subset of realizations including the minimal number of realizations.

The present disclosure provides systems and methods that expedite the design of physical components through the use of iterative and computationally efficient virtual simulations. In particular, the systems and methods of the present disclosure can be used as part of an iterative design process in which a product designer is able to iteratively make changes to a component design by iteratively interacting a visualization of a virtual representation of the component within a virtual environment.

Samples of metrics measured on physical devices are selected from a larger number of samples. The samples are selected based on the distributions of the measured metrics. A set of model instances are constructed that correspond to the selected set of samples. The model instances have parameters, which are set such that simulation of the model instances using the parameters predicts metrics that match the measured metrics from the set of samples. The principal components of the variances of the parameters is calculated. Non-linear models are fitted to the parameter variances as a function of the principal components. Statistical variations of the principal components are applied to the non-linear models to yield statistical variations in the parameters; and these are applied to simulations of model instances to yield statistical variations of a property of the device being simulated.

A method and apparatus for forecasting oil production from an oil well in a geological formation includes receiving a plurality of sets of predicted geological data, for each of the plurality of sets of predicted geological data, determining a probability for the predicted geological data of the formation, iteratively selecting one of the plurality of sets of predicted geological data using Monte Carlo sampling based on the determined probabilities, assigning the selected set of predicted geological data to a cluster of historical data, and for each set of historical data of the cluster generating a predicted oil production rate as a function of time utilizing a machine learning based oil model, generating, based on the predicted oil production rates, a forecasted oil production rate, determining, based on the forecasted oil production rate, a preferred operating parameter for the well, and operating based on the preferred operating parameter.

A non-transitory computer-readable recording medium storing a timing library creation program of causing a computer to execute processing, the processing including: extracting, from a delay variation database that stores delay variation values of gates included in circuit design data, a delay variation value, out of the delay valuation values matching to characteristics which are characteristics of one of signal paths in the circuit design data and which include a threshold voltage, a drive force, and a number of gate stages of the signal path; calculating an extended delay variation coefficient based on the extracted delay variation value and the characteristics; and creating, based on a basic timing library in which the delay variation value is not reflected and the extended delay variation coefficient, an extended timing library in which the delay variation value is reflected.

A device and method for building life cycle sustainability assessment using probabilistic analysis method, the device and method being capable of assessing and predicting building life cycle sustainability, and a recording medium storing the method. The device includes: a first storage unit storing a reference environmental impact assessment value of a reference building, and first and second environmental impact coefficient groups; an input unit receiving area information, amounts of building materials and energy sources; a probability distribution calculating unit storing a set value, and deducing probability distributions of the building materials and the energy sources; a first arithmetic unit calculating probability distributions of first and second environmental impact assessment values, and a probability distribution of a life cycle environmental impact assessment value; and a first output unit deducing a probability distribution of an environmental impact index, and outputting the deduced probability distribution of the environmental impact index.

Methods, systems and media for simulating or analyzing voltage drops in a power distribution network can use an iterative approach to define a portion of a design around a victim to capture a sufficient collection of aggressors that cause appreciable voltage drop on the victim. This approach can be both computationally efficient and accurate and can limit the size of the data used in simulating dynamic voltage drops in the power distribution network.

A method for remaining useful life prediction includes generating parameter data related to a performance of an electro-mechanical element. The method includes generating simulated behavior data of the electro-mechanical element by executing a digital-twin simulation model based on estimated operating conditions, and generating deviation data that characterizes how the parameter data deviates from the simulated behavior data. The deviation data includes a deterministic component and a stochastic component. The method includes generating extrapolated deviation data by extrapolating the deterministic component and the stochastic component of the deviation data forward in time, calculating a remaining useful life of the electro-mechanical element in response to the extrapolated deviation data, and reporting the remaining useful life to a person associated with the vehicle.