A method of modelling system behaviour of a physical system, the method including, in one or more electronic processing devices obtaining quantified system data measured for the physical system, the quantified system data being at least partially indicative of the system behaviour for at least a time period, forming at least one population of model units, each model unit including model parameters and at least part of a model, the model parameters being at least partially based on the quantified system data, each model including one or more mathematical equations for modelling system behaviour, for each model unit calculating at least one solution trajectory for at least part of the at least one time period; determining a fitness value based at least in part on the at least one solution trajectory; and, selecting a combination of model units using the fitness values of each model unit, the combination of model units representing a collective model that models the system behaviour.

A method for optimizing component type arrangement in which multiple component types are optimally disposed on multiple installation positions when an automatic feeder device which automatically loads the component storage tape, a manual feeder device which does not automatically load the component storage tape, and a reel holding device are installed into the installation positions on a common pallet, the method includes a step of determining a portion of the multiple installation positions as a fixed position and fixing the determined automatic feeder device to the fixed position; and an optimizing step of performing a simulation optimally disposing the multiple component types on the multiple installation positions under a condition that the manual feeder device can be moved to an arbitrary installation position other than the fixed position without moving the automatic feeder device from the fixed position.

A method includes: accessing first storage configured to store a first weight coefficient group which is at least some of a plurality of weight coefficients indicating a magnitude of interaction between a plurality of state variables in an evaluation function representing energy of an Ising model; accessing a plurality of second storages each of the plurality of second storage being configured to store a second weight coefficient group related to a state variable having a value of 1 in any of a plurality of state variable groups respectively including the plurality of state variables among the plurality of weight coefficients; outputting, for each of the plurality of state variable groups, a search result obtained by performing searching processing configured to perform processing of searching for an optimum solution by repeatedly performing a first update process with a first constraint or a second update process with a second constraint.

A logic circuit (for providing a multibit flip-flop (MBFF) function) includes: a first inverter to receive a clock signal and generate a corresponding clock_bar signal; a second inverter to receive the clock_bar signal and generate a corresponding clock_bar_bar signal; a third inverter to receive a control signal and generate a corresponding control_bar signal; and a series-chain of 1-bit transfer flip-flop (TXFF) circuits, each including: a NAND circuit to receive data signals; and a 1-bit transmit gate flip-flop (TGFF) circuit to output signals Q and q, and receive an output of the NAND circuit, the signal q from the TGFF circuit of a preceding TXFF circuit in the series-chain, the clock_bar and clock_bar_bar signals, and the control and control_bar signals; and the first transfer TXFF circuit in the series-chain being configured to receive a start signal in place of the signal q from an otherwise preceding TGFF circuit.

A method, according to some implementations, includes obtaining trial design simulation results for a set of trial designs and determining a set of Pareto designs in the set of trial designs based at least in part on the trial design simulation results and one or more performance parameters. The method further includes determining a set of convex hull designs in the set of trial designs, determining a set of recommended designs based at least in part on the set of Pareto designs and the set of convex hull designs, and transmitting the set of recommended designs.

A machine learning-based method for designing a high-strength high-toughness steel, including: (S1) obtaining data and filling in missing parts to form a data set; (S2) selecting feature data in the data set to form a standard data set; (S3) constructing two machine learning models of the high-strength high-toughness steel; (S4) completing training after the two models are evaluated to be qualified; (S5) finding frontier points, drawing a Pareto front, and distinguishing a known region and a feature space; (S6) in the feature space, setting a step for the feature data, drawing a grid space, and performing multiple training predictions on each grid point by using the models, to obtain predicted Gaussian distributions of two objectives; and (S7) searching for an expected improvement point through an efficient global optimization algorithm, and obtaining design parameter values of corresponding features.

A method including obtaining information about a trailer that has been partially loaded with preloaded stacks in a manner that deviates from an original load design. The trailer is loaded with stacks of pallets including (i) the preloaded stacks that have already been loaded in the trailer and (ii) unloaded stacks that have not yet been loaded into the trailer. The method also can include determining positions of empty floor spots remaining in the trailer. The method additionally can include determining a first portion of an incremental load design for the unloaded stacks using a gap-filling pattern behind an uneven rear edge of the preloaded stacks in the trailer. The method further can include determining a second portion of the incremental load design. The method additionally can include updating the incremental load design based on an overall load design of the trailer using a first simulated annealing using a first neighborhood defined by separate rows within a delivery group that does not include the preloaded stacks. The method further can include outputting at least the incremental load design, as updated, to cause the unloaded stacks to be loaded in the trailer according to the incremental load design while the preloaded stacks remain in the trailer. The incremental load design specifies a respective floor spot assignment for each of the unloaded stacks. Other embodiments are described.

A method includes receiving a pattern layout for a mask, shrinking the pattern layout to form a shrunk pattern, determining centerlines for each of a plurality of features within the shrunk pattern, and snapping the centerline for each of the plurality of features to a grid. The grid represents a minimum resolution size of a mask fabrication tool. The method further includes, after snapping the centerline for each of the plurality of features to the grid, fabricating the mask with the shrunk pattern.

Each constraint term in an expression representing energy in a combinatorial optimization problem is input the input means 71. The automatic establishment rate calculation means 73 calculates an automatic establishment rate for each constraint term, wherein the automatic establishment rate is a probability that a constraint represented by a constraint term is satisfied when all other constraints associated with individual spins associated with the constraint term are satisfied. The energy increase amount determination means 74 determines amount of energy increase at constraint breakdown for each constraint term, wherein the amount of energy increase at constraint breakdown is amount of energy increase when a constraint represented by a constraint term is no longer satisfied. The spin number derivation means 75 derives the number of spins associated with a constraint represented by a constraint term, for each constraint term.

A method for determining trial designs is provided. The method includes obtaining simulation data for a set of trial designs. The simulation data includes performance parameters and performance parameter values associated with each design in the set of designs for a set of criteria; determining an optimality criteria for evaluating the trial designs; searching, within the set of trial designs, for globally optimum designs based on the optimality criteria; and recommending globally optimum designs.