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.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using generative design processes, A method includes obtaining, by a computer aided design program, a design space for a modeled object, one or more design criteria for the modeled object, one or more in-use load cases, and a critical fatigue crack length for a material from which A physical structure will be manufactured; iteratively modifying a generatively designed three dimensional shape of the modeled object in the design space in accordance with the critical fatigue crack length for the material, wherein the iteratively modifying comprises enforcing a design criterion that limits a minimum thickness of the generatively designed three dimensional shape, the minimum thickness being based on the critical fatigue crack length for the material.

A method for establishing a variation model related to circuit characteristics for performing circuit simulation includes: performing first, second, third, and fourth Monte Carlo simulation operations according to a first netlist file and predetermined process model data to generate a first, a second, a third, and a fourth performance simulation results, respectively, where the first netlist file is arranged to indicate a basic circuit in a circuit system; and execute a performance simulation results expansion procedure according to the first, the second, the third, and the fourth performance simulation results to generate a plurality of performance simulation results to establish the variation model, for performing the circuit simulation to generate at least one circuit simulation result according to one or more performance simulation results among the plurality of performance simulation results, where the number of the plurality of performance simulation results is greater than four.

The present document relates to audio source coding systems. In particular, the present document relates to audio source coding systems which make use of linear prediction in combination with a filterbank. A method for estimating a first sample (615) of a first subband signal in a first subband of an audio signal is described. The first subband signal of the audio signal is determined using an analysis filterbank (612) comprising a plurality of analysis filters which provide a plurality of subband signals in a plurality of subbands from the audio signal, respectively. The method comprises determining a model parameter (613) of a signal model; determining a prediction coefficient to be applied to a previous sample (614) of a first decoded subband signals derived from the first subband signal, based on the signal model, based on the model parameter (613) and based on the analysis filterbank (612); wherein a time slot of the previous sample (614) is prior to a time slot of the first sample (615); and determining an estimate of the first sample (615) by applying the prediction coefficient to the previous sample (614).

Techniques, systems, and devices are described for providing a computational frame for estimating high-dimensional stochastic behaviors. In one exemplary aspect, a method for performing numerical estimation includes receiving a set of measurements of a stochastic behavior. The set of correlated measurements follows a non-standard probability distribution and is non-linearly correlated. Also, a non-linear relationship exists between a set of system variables that describes the stochastic behavior and a corresponding set of measurements. The method includes determining, based on the set of measurements, a numerical model of the stochastic behavior. The numerical model comprises a feature space comprising non-correlated features corresponding to the stochastic behavior. The non-correlated features have a dimensionality of M and the set of measurements has a dimensionality of N, M being smaller than N. The method includes generating a set of approximated system variables corresponding to the set of measurements based on the numerical model.

A global optimization tool may be used to predict characteristics of a multiple ply layered composite as a condition of one or more continuous variables and/or one or more binary variables. For example, the global optimization tool may predict characteristics of a composite for a large range of fiber orientation angles of each of layer of the ply. The optimization tool may include solving a mixed integer nonlinear programming (MINLP) model to obtain a multiple ply layered composite design that is optimized relative to objectives, such as areal weight and cost. Thus, the global optimization tool may be able to identify composite designs with lower areal weight and/or lower cost than the composite designs identified by prior art trial and error methods or heuristic algorithms. When a composite design is identified as meeting certain criteria that are input to the global optimization tool, that composite design may be manufactured.

Systems and methods for semiconductor design evaluation. IC layout information of a circuit design is received, and the circuit design is decomposed into smaller circuit pieces. Each circuit piece has IC layout information and a netlist. For each circuit piece, a set of strike models is selected based on the layout information and the net-list of the circuit piece and received radiation environment information. Each strike model has circuit components with voltage values corresponding to a respective particle strike. For each selected strike model of a circuit piece: a radiation susceptibility metric is determined by comparing functional results of simulation of the of the strike model with functional results of simulation of the circuit piece. For each circuit piece, a radiation susceptibility metric is determined based on the radiation susceptibility metrics generated for each selected strike model of the circuit piece.

A computer-implemented method for generating a prediction model for predicting rotor blade damages of a wind turbine is provided, wherein the method provides data including data sets for wind turbines, where each data set includes respective values of turbine variables(s), weather variable(s) and damage variable(s) wherein the method includes: a) discretizing the values, resulting in modified data sets; b) structure learning of a plurality of Bayesian networks based on the modified data sets, where each Bayesian network is learned by another learning method; c) determining an optimum Bayesian network based on a performance measure reflecting the prediction quality of a respective Bayesian network, where the optimum Bayesian network has the best performance measure; d) parameter learning of the optimum Bayesian network based on the modified data sets, resulting in conditional probabilities, where the optimum Bayesian network combination with the conditional probabilities is the prediction model.

In a simulation system and method thereof, the simulation includes, when a function value for a nominal point (NP) of an input is a first value, running a first simulation on the input; and when the function value for the NP of the input is a second value different from the first value, running a second simulation on the input. Here, the running of the second simulation may include (a) setting a boundary of an input distribution for the second value as a first distribution value, (b) generating input samples within the set boundary of the input distribution, (c) obtaining a worst case point (WCP) for the input by performing machine learning on the generated input samples, and (d) repeatedly performing the steps (a) to (c) while shifting the boundary of the input distribution until the boundary of the input distribution reaches a minimum critical value.

A method for processing a well data log may comprise adding one or more boundary areas to the well data log, dividing the well data log into one or more segments using the one or more boundary areas, processing each of the one or more segments on one or more information handling systems, and reforming each of the one or more segments into a final simulation. A system for processing a well data log may comprise one or more information handling systems in a cluster. The one or more information handling systems may be configured to perform the method for processing the well data log.