#computationalmodels

[govindhtech]

Causally Indefinite Computation cuts Boolean function query

Uncertain cause Computational Queries Are Simplified by Causal Indefiniteness in Quantum Computing

Causally indefinite computation can reduce query complexity, unlike typical computational models that use a fixed, sequential order of operations, according to recent studies. The study suggests that calculations without a causal structure can perform better. Traditional computational complexity assumes operations are ordered. However, the researchers found that “causally indefinite” processing, where the order is not predetermined, can benefit particular workloads. This study of non-deterministic computational models creates a new theory.

Classical Benefit: A causally indefinite classical-deterministic computer counterpart was investigated first. The authors demonstrated how causal indefiniteness simplified deterministic searches for a Boolean function (f_{6c}). For this 6-bit function, the typical deterministic query complexity is 4, meaning a sequential model needs at least 4 queries. However, a causally indefinite classical-deterministic process based on the Lugano process may calculate the function in three queries, indicating a generalised deterministic query complexity of D^{Gen}(f_{6c}) = 3. Using a recursive structure, the (f_{6c}) function was iterated to increase the constant spacing (4 vs. 3). The recursive construction yielded a polynomial separation for {f_l}_l, proving that (D^{Gen}(f_l) = O(D(f_l)^{0.792\dots})) as (D(f_l) \to \infty). In classical computing, causal indefiniteness benefits asymptotic computing.

Extension of Quantum Advantage

Building on classical findings, the researchers investigated quantum systems. They demonstrated a consistent quantum query complexity advantage for a modified 6-bit Boolean function (f_{6q}), derived from (f_{6c}). Modified Lugano process, a causally indefinite quantum supermap, computes (f_{6q}) in 3 quantum queries (Q_E^{Gen}(f_{6q})=3), while sequential quantum supermaps require 4 queries (Q_E(f_{6q})=4)). This shows that causally indefinite supermaps reduce quantum query complexity.

Method and Effects:

Boolean function query complexity was used to compare calculations with and without causal structure. The process function formalism modelled classical-deterministic processes. Indeterminate causal order quantum operations were simulated using quantum supermaps. The Lugano process, a well-known causally indeterminate classical-deterministic process that laid the groundwork for computer models, demonstrated the benefit. Lower constraints for deterministic query complexity, such as polynomial and certificate bounds, remained valid in the generalised framework of causally indefinite classical computation, helping identify candidate functions with an advantage.

Future challenges and prospects:

Even if an asymptotic polynomial advantage was obtained in the classical setting, comparable recursive procedures could not directly amplify the constant quantum advantage acquired. The causally indeterminate quantum computation's output state is not “clean” and contains leftover input information, hindering recursive composition like a subroutine. The main impediment to quantum advantage maximisation is this. Future study will focus on cleaning up these computations to perhaps maximise causal indefiniteness for recursive amplification. An asymptotic distinction in quantum query complexity is unclear in this scenario. Researchers suggest investigating partial Boolean functions, which may have even greater benefits, and more general classical processes: non-deterministic, stochastic. This paper proves that embracing causal indefiniteness reduces query complexity for specific issues in both classical and quantum worlds, paving the way for non-deterministic computation and innovative quantum algorithms. The French National Research Agency funded the study.

[servotechinc78]

Finite Element Analysis (FEA) Engineering Services: Enhancing Product Development and Structural Integrity

Finite Element Analysis (FEA) has become an indispensable tool in modern engineering, enabling the simulation and analysis of complex structures and systems under various conditions. By breaking down intricate geometries into smaller, manageable elements, FEA allows engineers to predict how products will react to real-world forces, vibrations, heat, and other physical effects. This article delves into the significance of FEA engineering services, their applications, benefits, and the process involved, with a particular focus on the offerings of Servotech Inc.​

Understanding Finite Element Analysis (FEA)

FEA is a computational technique used to approximate the behavior of physical systems. It involves subdividing a complex structure into finite elements—small, simple shapes like triangles or quadrilaterals in 2D, and tetrahedrons or hexahedrons in 3D. By applying known material properties and boundary conditions, engineers can solve the governing equations for each element, thereby predicting the overall behavior of the entire structure.​

Applications of FEA Engineering Services

FEA engineering services are utilized across various industries to address a multitude of challenges:​

1.Structural Analysis: Assessing stress, strain, and deformation in components to ensure they can withstand operational loads without failure.​

2.Thermal Analysis: Evaluating temperature distribution and heat flow within systems to prevent overheating and ensure thermal efficiency.​

3.Dynamic Analysis: Studying the response of structures to time-dependent loads, such as vibrations and impacts, to mitigate resonance and fatigue issues.​

4.Fluid-Structure Interaction: Analyzing the interaction between fluids and solid structures, crucial in designing efficient aerospace and automotive components.​

5.Electromagnetic Analysis: Investigating electromagnetic fields within devices to optimize performance and ensure compliance with regulatory standards.​

Benefits of FEA in Engineering

The integration of FEA into the engineering design process offers several advantages:​

Cost Reduction: By identifying potential issues early in the design phase, FEA minimizes the need for physical prototypes, thereby reducing material and labor costs.​

Enhanced Performance: FEA enables optimization of designs for weight, strength, and durability, leading to superior product performance.​

Risk Mitigation: Predicting failure modes and identifying critical stress points help in designing safer products, thereby reducing liability and warranty claims.​

Accelerated Development: Virtual testing through FEA shortens the product development cycle, allowing faster time-to-market.​

The FEA Process at Servotech Inc.

Servotech Inc. offers comprehensive CAD/FEA design and analysis services, employing a systematic approach to ensure accurate and reliable results:​

Pre-Processing:

Geometry Creation: Utilizing CAD software tools such as AutoCAD, Inventor, SolidWorks, and Creo, Servotech designs mechanical systems using 3D solid modeling, adhering to geometric dimensioning and tolerancing standards.

Material Properties: Defining material characteristics, including elasticity, plasticity, thermal conductivity, and density, to accurately simulate real-world behavior.​

Loads and Boundary Conditions: Applying external forces, pressures, thermal loads, and constraints to replicate operational environments.

Discretization and Mesh Generation:

Mesh Creation: Dividing the geometry into finite elements, ensuring appropriate element size and shape to balance accuracy and computational efficiency.​

Mesh Refinement: Enhancing mesh density in critical areas to capture stress concentrations and intricate details.​

Solution:

Physics and Assumptions: Selecting the appropriate analysis type—structural, thermal, fatigue, vibration, or buckling—based on the problem's nature.​

Equation Formulation: Generating FEA equations and matrices that represent the physical behavior of the system.​

Analysis Execution: Running linear or non-linear analyses, depending on material behavior and load conditions, through interactive or batch processing.​

Post-Processing:

Result Evaluation: Interpreting simulation outcomes, such as stress distributions, deformation patterns, temperature gradients, and natural frequencies.​

Visualization: Presenting results through contour plots, graphs, and animations to facilitate comprehensive understanding.​

Sub-Modeling: Focusing on specific areas of concern within large models to obtain detailed insights.​

Servotech Inc.'s Expertise in CAD/FEA Design and Analysis

Servotech Inc. leverages advanced CAD and FEA tools to deliver precise engineering solutions:​

Integrated Approach: Combining 3D solid modeling with FEA allows for seamless design iterations and optimization.​

Comprehensive Simulations: Conducting simulations to analyze stress, pressure, temperature, and flow velocity distributions over space and time, ensuring designs meet performance criteria.​

Hardware-in-the-Loop (HIL) Testing: Integrating FEA models with controllers for HIL testing visualization, enabling real-time validation of control strategies.

Case Study: Hydrostatic Transmission Control

An example of Servotech's application of FEA is the hydrostatic transmission control system

Design and Modeling: Developing a 3D model of the transmission system, incorporating all mechanical components and interfaces.​

FEA Simulation: Analyzing stress distribution and deformation under various load conditions to ensure structural integrity and performance.​

Optimization: Refining the design based on simulation results to enhance durability and efficiency.​

Conclusion

FEA engineering services by servotech play a pivotal role in modern product development, offering insights that drive innovation, safety, and efficiency. Servotech Inc.'s expertise in CAD/FEA design and analysis exemplifies the effective application of these techniques, providing clients with optimized solutions tailored to their specific needs. By embracing FEA, industries can achieve superior

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[evoldir]

Fwd: Postdoc: UWyoming.2.ComputationalModeling

Begin forwarded message: > From: evoldir@web.rhpcs.mcmaster.ca > Subject: Postdoc: UWyoming.2.ComputationalModeling > Date: 11 December 2021 at 07:43:30 GMT > To: rdmpage@gmail.com > > > We are seeking two additional postdoctoral researchers to join > our interdisciplinary data science team of eight faculty and > over 13 postdocs, spanning multiple research areas in ecology > and evolutionary biology. As part of the modelscape consortium > (https://ift.tt/3GzpgWt), the postdoctoral > researchers will work closely with one or more faculty members at the > University of Wyoming: Alex Buerkle, Sarah Collins, Daniel Laughlin, > Lauren Shoemaker, and Topher Weiss-Lehman. > > Dramatic increases in the scale and availability of data are profoundly > reshaping all domains in the life sciences. Data acquisition and > availability from DNA sequencers, environmental sensors, parallel > global studies, and imagery are outpacing our capacity for analysis, > including the development of models that represent our knowledge of > biological processes. Research in our consortium is developing and > competing computational, statistical, and machine learning methods for > multi-dimensional data to create predictive and explanatory models for the > life sciences. The project focuses on three research areas: (1) connecting > genome to phenome (particularly in the context of evolutionary biology), > (2) mechanistic modeling of species interactions and community diversity, > and (3) time series of material and energy flux in aquatic ecosystems. > > The positions are 100% research with flexible start dates; however, > preference will be given to candidates who will be able to join the > consortium immediately. The positions are for two years, with the > possibility of extending the appointment, contingent upon performance. > > The postdoctoral researchers will be primarily based in one or a few > labs but will benefit from the opportunities to collaborate broadly. The > positions allow for multiple professional development opportunities, > including training in highly interdisciplinary science, collaborations > across institutions, regular meetings with the entire consortium, > mentorship toward academic and non-academic career development, and > interactions with graduate and undergraduate students. > > Successful applicants are not expected to have expertise in all facets > of the project, but rather may be experts in a given domain of the > life sciences or area of modeling. The postdoctoral researchers will > primarily analyze existing and simulated data, and will have additional, > complementary opportunities for laboratory or field research. We recognize > that the best science can originate from diverse collaborations with > people from varied backgrounds, and we especially encourage applicants > from underrepresented groups to apply. The positions are supported by > a 4-year, $6 million NSF EPSCoR RII Track-2 grant in response to our > proposal entitled 'Highly predictive, explanatory models to harness the > life science data revolution'. > > MINIMUM QUALIFICATIONS:    > Completion by the position start date of all requirements for a PhD in > ecology, evolutionary biology, environmental science, statistics, computer > science, mathematics, complex systems science, or a related field.  > > DESIRED QUALIFICATIONS:      > In the cover letter, applicants should state clearly and illustrate how > their experience and interests match the following preferred > qualifications. > 1.      Record of publishing in peer-reviewed literature > 2.      Excellent verbal and written communication skills > 3.      Experience in at least one of the following research areas: (a) >        connecting genome to phenome, or other aspects of evolutionary >        genetics, (b) mechanistic modeling of species interactions, >        population dynamics, and community diversity, or (c) examining >        material and energy flux in aquatic ecosystems > 4.      Has a keen interest in developing skills in mathematical or >        statistical modeling to extend strong conceptual thinking and >        research in life sciences > 5.      Previous interdisciplinary and collaborative work, in addition >        to project leadership > 6.      Interest in working with a diverse team across disciplinary >        boundaries > > REQUIRED MATERIALS:   > Complete the online application and submit a cover letter stating your > interest in the position and previous experience as it relates to the > position, including each of the preferred qualifications. Also, provide > a CV, links to 1–2 recent first-authored publications, and names and > contact information for three professional references.  Please apply here: > https://ift.tt/3IEX9XO > > > > Alex Buerkle > Department of Botany > University of Wyoming > Laramie, WY 82071, USA > https://ift.tt/31RJnjX > buerkle@uwyo.edu > > Alex Buerkle > via IFTTT

[sciencenterfsl]

We had an awesome time at Cornell last Saturday with our Coding for All team touring their labs and learning about their research!

[iamsrutijain-blog]

Designing neural network: What activation function you plan to implement

Designing neural network: What activation function you plan to implement

Activation function defines the way the output is manipulated based on the input in a Neural Network. These functions must not only be differentiable but they are also mostly non-linear for determining a non-linear decision boundary using non-linear combinations of the input feature vector and weights. A few options for choosing activation function and their details are as under:

Identify…