Technology
Our technology and the WEIVI process
How do WEIVI’s technology and the WEIVI process work?
WEIVI’s technology is based on state-of-the-art academic research. Generally, the path from theory and innovations to commercial applications takes nearly half a century, but within the WEIVI team we have a vast understanding of both theoretical and applied sciences that makes it possible to shorten this process continuously. We take feedback from our customers and challenge ourselves to keep innovating and creating new shortcuts from theory to real life applications and solutions. WEIVI enables its customers to utilise future technologies way ahead of time.
WEIVI’s way to use scientific computing and mathematical modelling in creating extensive long-term forecasts and in optimisation of dynamic systems is in a class of its own. With our digital twin technology we can effectively simulate alternative scenarios and demonstrate causal relationships between different phenomena.
Our software, built on the WeiviEngineᵀᴹ, elevates our customer’s knowledge-based management and decision-making to an entirely new level. With our software you can lead your organisation towards a resource-wise future with maximal performance and efficiency. This performance is delivered with a significantly lower power consumption than AI.
WEIVI’s process
Our process begins by mapping the needs and goals of our customer. Through mathematical modelling we find the most relevant path to achieve your goals, taking into account your data, information system architecture, and other limitations. We construct a personalised model which becomes the basis of your own WeiviEngine — the computational core of our scientific computing program. The core then creates simulations utilising digital twins and produces forecasts necessary for the optimisation of your organisation.
The computational core is embedded into a software shell that is connected to the data sources and the user interface that fulfils the customer’s needs.
WeiviEngine vs. AI
AI, a powerhouse in handling static problems, faces a formidable challenger in the form of WEIVI when it comes to dynamic simulations, long-term deep-analytical forecasts, and optimisation. Powered by WEIVI’s digital twin technology, grounded in scientific computing, we excel at simulating alternative scenarios and illuminating causal relationships among diverse phenomena.
In terms of accuracy, WEIVI’s scientific computing core stands as a paragon of precision. AI, particularly its neural networks, tends to inaccuracies and often demands retraining when errors emerge. This stems from AI’s emulation of human brain functions.
Speed is another dimension where WEIVI shines, delivering swift results, especially for intricate dynamic challenges. Conversely, AI, especially neural network models, may lag behind, posing challenges in time-sensitive domains like autonomous vehicles, where rapid decision-making is paramount.
The traceability of errors is pivotal. AI’s errors often necessitate extensive retraining or starting from scratch, with no assurance of preventing recurrence. As the result of inadequate training and the inherent structure of AI programs, self-driving cars have been in serious accidents, the causes of which are largely untraceable. In sharp contrast, In the core of WEIVI’s software is a mathematical model based on scientific computing where unwanted results are easily and accurately determinable as either an error in the calculation or the logic without fear of repetition.
AI’s quest for comprehensive datasets to prevent fatal accidents raises questions about the massive scale and nature of the required data. In contrast, WEIVI’s scientific computing approach doesn’t rely on such massive datasets, streamlining the process and avoiding the need for exhaustive data collection.
Energy efficiency is a critical consideration. Scientific computing and WEIVI excel due to mathematical precision, speed, and traceability, consuming significantly less energy than AI programs. The energy efficiency of AI is compromised during both training and operation, making it an environmentally taxing choice. To put this in perspective, the energy used to train a single neural network program could power WEIVI’s software for ten daily runs spanning a thousand years.
In summary, the distinctions between scientific computing, exemplified by WEIVI, and AI become evident when considering predictive capabilities, accuracy, speed, energy efficiency, and traceability. While AI has achieved remarkable feats, scientific computing remains the preferred choice for efficiently and accurately addressing complex, dynamic problems. The synergy between human intelligence and computational power continues to prove formidable in tackling a diverse array of challenges.
Our technology
Long term deep-analytical forecasts
With WEIVI long term deep-analytical forecasts you can detect any future challenge or any deviations from your desired outcome well in advance. You get as much time as possible to plan for the necessary measures to fix your course.
WEIVI’s forecasts are based on mathematical models which reveal the objective regularities and causal relationships within your data. The models are analysed with computer-assisted scientific computing. Similar methods are employed in weather forecasts, where observational data of thousands of weather measurement apparatuses from past and present are entered into the model for a computer to calculate the probable weather developments.
Simulating with predictive digital twins
WEIVI takes simulating with digital twins a step ahead with its unique predictive features. Once we determine the desired outcomes, we can leverage scientific computing in tandem with the digital twin to identify high-impact solutions.
A digital twin is a construct of a real-life system within a computational framework. However, the digital twin alone is not sufficient for creating forecasts in a dynamic environment. You also need a mathematical model that combines relevant real life data with objective regularities within the scenario and framework parameters. The heart of WEIVI’s predictive digital twin simulations is WeiviEngine, our scientific computing core, which calculates and produces predictions and forecasts based on your desired variables.
WEIVI’s predictive digital twin technology enables actual forecasting surpassing the mere data visualisations of AI powered digital twins. WEIVI allows you to create scenarios and test their susceptibility to different initial values with ease and fine-tune solutions. The simulation shows for instance how the prices of raw materials, increases in employee salaries, changes in interest rates, production equipment malfunctions, or contract price increases affect your organisation’s bottom line.
Dynamic optimisation of complex systems
WEIVI´s dynamic optimisation of complex systems leverages predictive digital twin technology and enables solving complex problems efficiently. This means we can quickly respond to changes and manage continuously changing complex interactions. WEIVI’s technology harnesses scientific computing and mathematical modelling, which is the only efficient way to solve complex dynamic phenomena.
Dynamic vs. static optimisation – Choose the right technology
In dynamic phenomena a system may change significantly in time while static systems are time-independent. In static optimisation regularities are fixed, making them easily detectable by artificial intelligence. Complex interactions within dynamic systems cannot be understood with static methods, and steering the system towards a desired outcome is simply impossible.
In our dynamic optimisation process both true real-time information and deviations from expectations are fed into WeiviEngine which manages the digital twin environment, which enables efficient optimisation and a smooth process.
Railway Traffic – A Case for dynamic optimisation of complex systems
In railway traffic for example, during switch malfunction or commonplace delays WeiviEngine calculates the schedule and track changes, speeds, and new encounters for traffic control in order to minimise delays and multiplicative effects. Traffic can flow better and the railway’s attractiveness as a mode of transportation is enhanced – strengthening sustainability. Dynamic optimisation enables more efficient use of the already existing railway network, and most importantly, it allows optimisation of railway traffic during serious breakdowns.
Scenarios
What is the best way to respond to an unforeseen issue? How and when can you best seize your opportunity? With WEIVI’s scenario analysis you can compare different scenarios and select the best approach for you.
Scenarios concerning internal or external changes within the digital twin can be applied in many vastly different situations. With our technology you can evaluate organisational crisis resilience and prepare for crises well in advance. Our scenarios are suitable to use in a multitude of different applications, such as city or traffic infrastructure.
Using scenarios within WEIVI’s digital twin environment you can achieve significant savings in terms of both energy and infrastructure costs. You can easily evaluate the impact of different investment options as a part of your investment planning, and see the results of your investment decisions in advance.
Impact evaluation
In impact evaluation it is essential to know the different available choices and options, their possible outcomes, and expected expenses. In WEIVI’s assessment process digital twin scenario outcomes are compared through computing processes, revealing the best results and options of different alternatives in relation to values essential to you. Impact evaluation can take you from your goals to the best results. Through the evaluation you learn how you can achieve the most optimal outcome through the allocation of your operational resources and investments.