Sentences with phrase «of nonlinear models»

Optimum Niño3.4 breakpoints are positive (> +0.4 °C) in the majority of the nonlinear models, confirming that ENSO / precipitation relationships differs between La Niña / neutral and El Niño winters.

Finite precision computer realizations of nonlinear models give unrealistic solutions because of deterministic chaos, a direct consequence of round - off error growth in iterative numerical computations.»

Two types of nonlinear model — both one - sided GEV regressions where linear relationships are allowed to differ above / below a Niño3.4 index threshold — are considered.

Not exact matches

When you run a financial model of the lifetime value of the customers of a business you will often see that the benefits of retention are nonlinear.

For example, Andrew Davis» model of the buyer's journey adapted from McKinsey reflects a cyclical, nonlinear buyer's journey.

A new article by the researchers suggests the brain uses nonlinear message - passing between connected, redundant populations of neurons that draw upon a probabilistic model of the world.

The program encompasses a wide range of theoretical perspectives, such as symbolic computation, connectionism, ecological, nonlinear dynamics, and complex systems, and a variety of methodologies including both experimental studies and modeling.

This workshop explored mathematical tools and problems in describing the life cycle, stage conversion, and clonal expansion of T. gondii by bringing together expertise in parasitic diseases, epidemiology, population genetics, disease modeling, network dynamics, evolutionary dynamics, and nonlinear analysis.

The framework is based on solving nonlinear coupled ordinary and partial differential equations that model the kinetics of the following phenomena: (1) mass transport in the electrolyte and electrode using the Nernst - Planck equation; (2) electrical potential distribution using the Poisson equation; (3) interfacial reactions that determine the boundary conditions or source terms (using the Butler - Volmer equation or constant - flux conditions); and (4) evolution of the electrode / electrolyte interface using the Allen - Cahn equation within the phase - field modeling (PFM) approach.

The core facility is a two - photon in vivo imaging platform developed at the Nonlinear bioimaging laboratory, a technique that allows for non-invasive structural and functional measurements in small animal models at different scales: from macroscopic imaging of the brain morphology to highly resolved microscopy of neuron populations, single neurons, and even subcellular structures.

Linear and nonlinear computational models must be validated in order to establish confidence in the prediction and understanding of tokamak disruption physics with and without mitigation.

Simple models are very different, and are more like an effective sensitivity and may well lack some of the nonlinear dynamics / regional processes that occur in the real world and, in a more limited way, in the complex models.

We previously showed that a simple linear ensemble - coding model of the SC motor map could fully account for the nonlinear properties of saccades [33].

They conclude, based on study of CMIP5 model output, that equilibrium climate sensitivity (ECS) is not a fixed quantity — as temperatures increase, the response is nonlinear, with a smaller effective ECS in the first decades of the experiments, increasing over time.

You see, the human body is a nonlinear system and things like the calories in versus calories out model — a linear system — at best, give us only a guide of how things will go.

The C - R - E-A-T-E-R model (Havelock & Zlotolow, 1995) provides teacher education faculty members with seven nonlinear steps that help them explore how the various components of the teacher education program operate and interrelate.

The role of positivity and connectivity in the performance of business teams: A nonlinear dynamics model.

But, on the basis of studies of nonlinear chaotic models with preferred states or «regimes», it has been argued, that the spatial patterns of the response to anthropogenic forcing may in fact project principally onto modes of natural climate variability.

Topics will include predictability, ensemble prediction, nonlinear prediction, nonlinear time series analysis, low - dimensional chaos, error growth in the models, nonlinear modeling, fractals and multifractals, bifurcation, and other aspects of nonlinear science.

I guess I don't understand how a climate model could reflect a linear expectation for centuries and also contain a trigger for a nonlinear collapse within the timeframe of the organizer on Al Gore's Blackberry.

For example, Hansen's recent paper on Scientific Reticence is quite explicit that much of important physics of ice sheets is not included in the models, hence his raising of matters to do with nonlinear behaviour (eg disintegration) of ice sheets.

• Lack of formal model verification & validation, which is the norm for engineering and regulatory science • Circularity in arguments validating climate models against observations, owing to tuning & prescribed boundary conditions • Concerns about fundamental lack of predictability in a complex nonlinear system characterized by spatio - temporal chaos with changing boundary conditions • Concerns about the epistemology of models of open, complex systems

O'Gorman, P. A., and T. Schneider, 2007: Recovery of atmospheric flow statistics in a general circulation model without nonlinear eddy - eddy interactions.

Nonlinear Single - Degree - of - Freedom Models in Earthquake Engineering.

Issues remain over the proper treatment of thermobaricity (nonlinear relationship of temperature, salinity and pressure to density), which means that in some isopycnic coordinate models the relative densities of, for example, Mediterranean and Antarctic Bottom Water masses are distorted.

Mind you — I should not encourage the idea that model runs do anything but diverge exponentially from each other due to the nature of the core nonlinear equations.

The well known example of Lorentz is true in a discretized deterministic nonlinear atmospheric model.

Given the range of possible outcomes of nonlinear processes in individual models — the usefulness of this is a matter of perspective.

Experience with solution algorithms, data assimilation methods and tools, coupling of components and processes, nonlinear and linear solvers, limiters, and / or other numerical issues common with complex codes within earth system models of varying complexity

If «[t] he inconvenient truth remains,» according to Philip Stott, that «climate is the most complex, coupled, nonlinear, chaotic system known,» then like flipping a coin, It will not matter if we devise a mathematical model to combine the data of the last 100 flips with a dataset reflecting the 100 flips before that — even if you consider want to consider how many tails you got on the previous 1,000 flips — the odds for the next flip still will be 50 - 50.

This spread results because the model equations provide a deterministic set of results that each can be different since the climate is a chaotic nonlinear system both in the model, and even more so in the real world.

The model output is evidence of the result of the many processes working together, much as the Pythagorean theorem provides evidence about the hypoteneuses of a large set imperfectly studied right triangles; or long term simulations of the planetary movements based on Newton's laws provide evidence that the orbits are chaotic rather than periodic; or simulations provide evidence that high - dimensional nonlinear dissipative systems are never in equilibrium or steady state even with constant input.

The other problem is a mathematical one, in terms of how you actually evaluate with observations a model with a very large number of degrees of freedom that is nonlinear / chaotic as well.

This, plus the fact that remarkable close simulations of the time series are obtained with a model consisting of a few nonlinear differential equations suggest the intriguing possibility that there are simple rules governing the complex behavior of global paleoclimate.»

Web, I say again as I said many times before that the modern view of mechanics is that nonlinear dynamical systems provide a deterministic method that has a lot of evidence that it does model turbulence and perhaps includes statistical physics.

It shows thousands of diverging solutions that is the defining property of these chaotic models that have at their core nonlinear equations of fluid transport.

In a system such as the climate, we can never include enough variables to describe the actual system on all relevant length scales (e.g. the butterfly effect — MICROSCOPIC perturbations grow exponentially in time to drive the system to completely different states over macroscopic time) so the best that we can often do is model it as a complex nonlinear set of ordinary differential equations with stochastic noise terms — a generalized Langevin equation or generalized Master equation, as it were — and average behaviors over what one hopes is a spanning set of butterfly - wing perturbations to assess whether or not the resulting system trajectories fill the available phase space uniformly or perhaps are restricted or constrained in some way.

In the long term, forward, process - based models of proxy formation are needed for explicitly representing multivariate, nonlinear, and potentially nonstationary relationships between the proxy and climate systems [Evans et al., 2013].

In this case in particular, the correct formulae are the full nonlinear Navier - Stokes equations with external forcings, implemented in a full thermal model of the Earth.

Any change in a model can produce divergent solutions that are not predictable beforehand — it is the nature of the nonlinear Navier - Stokes equations — this extends to the range of uncertainty in climate data and to the number and breadth of couplings.

One might (or might not) argue for such a relation if the models were empirically adequate, but given nonlinear models with large systematic errors under current conditions, no connection has been even remotely established for relating the distribution of model states under altered conditions to decision - relevant probability distributions... There may well exist thresholds, or tipping points (Kemp 2005), which lie within this range of uncertainty.

For one thing, they're linear models, in which the impacts of various factors (man - made greenhouse gases, ENSO, natural climate forcings) are additive, but while that is often a good approximation, the real world is nonlinear.

The biggest scientific contribution that Hansen and his colleagues make is an attempt to nail down a Moore's law (which models nonlinear rates of growth in computer chips) to ice sheets: Assuming non-linear processes have already begun, how fast will Greenland and Antarctica melt?

What the red team needs is an explanation in single syllables of what is and is not theoretically answerable using these high level nonlinear models.

Did you know that the exact same real world system can be modeled with the exact same feedback as either linear or nonlinear by choice of flow variables?

Depending on accumulation time - scale, the strength of evidence favours nonlinear models at 28 % — 30 % of stations when the Niño3.4 breakpoint is free to vary, versus just 3 % with a fixed breakpoint.

IMO, the standard 1D energy balance model of the Earth's climate system will provide little in the way of further insights; rather we need to bring additional physics and theory (e.g. entropy and the 2nd law) into the simple models, and explore the complexity of coupled nonlinear climate system characterized by spatiotemporal chaos.

Our atmosphere - ocean model shows that the freshwater spurs amplifying feedbacks that would accelerate ice shelf and ice sheet mass loss, thus providing support for our assumption of a nonlinear ice sheet response.

There are mathematical fatal flaws in all the models that can not be overcome even if supercomputers improve by an order of magnitude, and if Rob Ellisons nonlinear dynamic chaos concerns can be overcome by enough ensemble runs to discern their main climate strange attractors.

Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate.