I. Introduction

With the rapid rise of global digital transformation and intelligent development, the concept of “simulation” has continued to expand in both meaning and scope. As a bridge connecting the virtual and the real, simulation science and technology is increasingly demonstrating its key value across industries. Since the international science-and-technology organization Asia Simulation FederationFederation (亚洲仿真联盟) was officially established on December 21, 2024, it has advanced the idea that “Simulation for EverythingSimulation for Everything”, aiming to root itself in Asia while radiating globally—promoting research, technology, education, and application development in system modeling and simulation, and facilitating industrial collaboration. This has attracted growing attention from experts, scholars, engineers, and science-and-technology practitioners. Notably, during the First Global Simulation Conference held in Hangzhou, China (September 20–21, 2025), the idea of “Simulation for Everything” sparked broad discussion and lively debate among attendees from multiple fields.

On the occasion of the Asia Simulation Federation’s first anniversary, this article—written from a research perspective—attempts to systematically elaborate the origins, connotations, technological realization, and multidimensional societal impact of the idea that “Simulation for Everything.” It also discusses the distinctions and connections between this idea and related concepts such as virtual reality, and looks ahead to future directions of this trend. Through a comprehensive analysis of the “Simulation for Everything” phenomenon, the article aims to better understand the nature of the current technological transformation, deepen public understanding of simulation technology, promote broader application of simulation, and jointly anticipate the arrival of a new simulation era.

 II. Origins and Theoretical Foundations of “Simulation for Everything”

 2.1 Early Philosophical Roots

The intellectual roots of “Simulation for Everything” can be traced back to ancient philosophical reflection on the nature of reality. In The Republic, the ancient Greek philosopher Plato proposed the famous “Allegory of the Cave,” describing prisoners confined in a cave who can only see shadows of real objects projected on a wall, mistaking those shadows for reality. This allegory raises a profound question: what humans perceive as “reality” may itself be a kind of “simulation” or “projection.” In ancient Chinese philosophy, Zhuangzi’s story of “Zhuangzi's Butterfly Dream” also expresses deep doubt about the boundary between reality and illusion, reflecting a simulation-oriented inquiry into the essence of the world.

In the 17th century, the French philosopher René Descartes further developed this line of thought through the “Evil Demon” Hypothesis in Meditations on First Philosophy. Descartes imagined a powerful demon capable of deceiving humans so that everything people perceive could be an illusion. This hypothesis laid the groundwork for later thought experiments such as the “brain in a vat,” and became an important philosophical source for modern simulation hypotheses.

 2.2 The Formation of the Modern Simulation Hypothesis

In 2003, British philosopher Nick Bostrom published the influential “Simulation Argument” in The Philosophical Quarterly, marking the formal formation of the modern simulation hypothesis. Bostrom proposed a logically rigorous trilemma:

1. Almost all civilizations that reach a posthuman stage go extinct.

2. Almost all posthuman civilizations are not interested in running ancestor simulations.

3. We are almost certainly living in a computer simulation.

Bostrom’s argument rests on the assumption of “substrate independence,” meaning that mental states can be realized on multiple physical substrates rather than being limited to biological brains. If this assumption holds, then advanced civilizations could plausibly create virtual worlds containing conscious simulated beings.

Bostrom’s argument triggered extensive discussion, and many scholars have supplemented or criticized it from different angles. For example, in 2021, Micah Summers and Marcus Arvan published a paper in the Australasian Journal of Philosophy arguing that if panpsychism or panqualityism is true, then one could only live in a simulation as a “brain in a vat,” which greatly reduces the probability of the simulation hypothesis.

 2.3 Technology as a Driving Force

With the rapid development of computing technology, the idea that “Simulation for Everything” has gradually moved from philosophical speculation toward technological realization. In the latter half of the 20th century, computer simulation began to be widely applied in fields such as defense and aerospace—for example, U.S. military simulations of missile trajectories and battlefield environments.

The emergence of the digital twin concept further advanced the development of “Simulation for Everything.” The concept was first proposed by Professor Michael Grieves of the University of Michigan, referring to the creation of a digital model in virtual space that corresponds fully to a physical entity. As the Internet of Things, big data, and AI have progressed, digital twin technology has expanded from industrial equipment to complex systems such as cities, ecosystems, and even the human body—making “Simulation for Everything” increasingly feasible.

 III. Technical Realization and Applications of “Simulation for Everything”

 3.1 Digital Twins: The Core Technology Enabling Simulating Everything

Digital twin technology is a key pillar supporting “Simulation for Everything.” By creating in virtual space a digital model that corresponds one-to-one with a physical entity, it enables accurate simulation and prediction of the real world. According to research by ZHAO Longfei and colleagues, a digital twin system consists of six basic elements: the physical entity, digital mapping, interaction mapping, twin data, twin knowledge, and twin intelligent applications.

From a technical perspective, digital twins require collaboration among multiple technologies. First, large numbers of sensors are needed to collect real-time data from the physical entity as inputs for the virtual model. Second, strong computing capability is required to run complex physical models and data-processing algorithms. Finally, advanced visualization techniques are needed to present simulation results in an intuitive way.

Digital twin technology has already been widely applied across multiple domains, including:

1. Ocean science cognition: By constructing a digital twin of the marine environment, it becomes possible to dynamically present and interact in real time with “3D space + time dimension + multi-dimensional marine environmental elements and phenomena.”

2. Ocean development and utilization: Digital twins can support full life-cycle management in scenarios such as ships, marine ranching, ports, offshore wind power, and oil and gas extraction—improving safety and efficiency in marine resource development.

3. Full life-cycle management of marine equipment: Through digital-twin modeling of marine equipment, comprehensive simulation and optimization can be achieved across design, manufacturing, operation and maintenance, and retirement stages—reducing total life-cycle cost.

4. Smart cities: Digital twin city models have been deployed in multiple cities worldwide. One example is the “Virtual Singapore” project, which optimizes urban planning, traffic management, and resource allocation through a city-scale digital twin.

 3.2 Breakthrough Applications of Quantum Computing in Simulation

As a next-generation computing technology, quantum computing offers unprecedented computational power for “Simulation for Everything.” Unlike classical computers based on binary bits, quantum computers use qubits as basic units of information. Leveraging quantum superposition and entanglement, quantum computing can achieve exponential acceleration for certain problems.

By 2025, quantum computing has achieved notable breakthroughs in simulation. Alibaba DAMO Academy’s “TaiZhang 3.0” quantum simulator reportedly achieved, for the first time globally, a full-amplitude simulation of 512 qubits, representing nearly a tenfold increase in computational scale compared with Google’s 53-qubit Sycamore. This breakthrough enables quantum computers to handle more complex molecular simulations and materials design problems.

Key application areas include:

1. Materials science: Quantum computers can precisely simulate electronic structures and chemical reaction processes, providing theoretical guidance for new materials. For example, quantum simulation could model electron behavior in high-temperature superconductors, accelerating progress toward room-temperature superconductivity.

2. Drug discovery: Quantum computers can simulate interactions between drug molecules and biomacromolecules, predicting binding energy and bioactivity to accelerate new drug development. Reports suggest quantum computing could shorten drug development cycles by 15 years.

3. Financial risk forecasting: Quantum computers can process large-scale complex data to improve market forecasting accuracy. For example, quantum computation could compress options-portfolio risk calculation from 3 hours to 8 seconds, greatly improving decision efficiency.

4. Computational fluid dynamics (CFD) simulation: In October 2024, a Chinese research team reportedly completed the world’s largest quantum CFD simulation using the “Benyuan Wukong” quantum computer, demonstrating quantum computing’s significant potential in engineering.

 3.3 AI-Driven Intelligent Simulation

Deep integration of AI and simulation is reshaping both the methods and capabilities of simulation. AI-driven intelligent simulation is mainly reflected in: 

1. AI-accelerated simulation computation: Machine-learning algorithms can significantly improve speed and efficiency. For example, the ANSYS SimAI platform can complete in minutes calculations that traditionally take hours, greatly improving design exploration efficiency.

2. AI optimization of simulation parameters: AI can automatically optimize simulation parameters, shortening simulation cycles by 30%–50%. In semiconductor chip design, this can speed up design iteration and bring time-to-market forward by about six months. In addition, QuiKTech’s AI chemistry laboratory can accept orders globally and run reagent-formula experiments around the clock, automatically producing analysis reports—greatly improving iteration speed and experimental efficiency.

3. AI-assisted model construction: AI can automatically build simulation models from historical data, reducing the threshold and cost of modeling. For example, the Altair HyperWorks 2025 platform deeply integrates an AI kernel, enabling a shift from “ex-post trial-and-error” to “ex-ante optimization.”

4. AI-driven virtual training systems: Combining AI with simulation enables new virtual training paradigms. For example, Shanghai Suochen Information Technology’s physical-AI virtual training system integrates multi-physics modeling and AI algorithms, boosting R&D efficiency by over 50%, and has been widely used in virtual training scenarios such as humanoid robots, low-altitude UAVs, and intelligent driving.

 3.4 Multi-Physics Coupled Simulation

Multi-physics coupled simulation is another key technical support for “Simulation for Everything.” It can handle interactions among multiple physical phenomena within complex systems, making comprehensive understanding and performance optimization possible.

Breakthrough progress has been achieved in multiple fields:

1. New energy: Multi-physics coupled simulation solves multi-field coupling challenges in battery management systems, improving comprehensiveness of simulation results. In new-energy vehicles, it reportedly improves BMS simulation accuracy to over 95%, effectively predicting battery life and safety, and helping increase EV range by 15%.

2. Aerospace: Multi-physics coupled simulation can model aerodynamic, thermal, and structural responses of aircraft in complex environments, providing comprehensive support for aircraft design. It is projected that by 2025, such simulation will be widely used in aerospace, with market share exceeding 30%.

3. Biomedicine: Multi-physics coupled simulation can model complex physiological processes inside the human body, supporting diagnosis and treatment. For example, a cardiac digital twin can accurately reproduce blood flow in chambers and vessels, valve opening and closing, myocardial contraction, and electrical-signal conduction—helping physicians develop more precise treatment plans.

 IV. Societal Impacts of “Simulation for Everything”

 4.1 Positive Impacts: Innovation and Efficiency Gains

The idea that “Simulation for Everything” has profound positive effects on societal development, mainly in:

1. Driving technological innovation and industrial upgrading

   Simulation provides low-cost, high-efficiency innovation platforms that accelerate breakthroughs and upgrading. In manufacturing, for example, digital twins can simulate the full product life cycle from design to production to maintenance, reducing physical prototype testing and significantly cutting R&D cost.

In aerospace, multi-physics coupled simulation has become a core tool for aircraft design. By simulating aerodynamic performance, thermal management, and structural response in virtual environments, issues can be discovered and solved before manufacturing, substantially reducing R&D risk and cost.

2. Improving the scientific basis and accuracy of decisions

   Simulation provides scientific evidence for decision-making in complex systems, shifting decisions from “experience-driven” to “data-driven.” For example, in urban planning, digital twins can simulate traffic flow, energy consumption, and environmental changes, providing evidence for planning and governance.

3. Reducing cost and risk

   Simulation can model many possible scenarios in virtual environments, helping organizations reduce cost and risk. In industrial production, companies can simulate production processes to identify and solve problems early, avoiding errors and losses in actual production. Statistics suggest that optimized production lines can reduce energy consumption by 10%–15%, cut material waste by 20%, and reduce overall manufacturing cost by about 8%.

4. Advancing scientific research and expanding human understanding

   Simulation provides new methods and tools for research, improving understanding of complex systems. In cosmology, for example, supercomputers can simulate cosmic evolution to test theories and reveal the universe’s mysteries.

In life sciences, molecular dynamics simulations can reveal biomolecular structures and functions, helping scientists understand fundamental life processes. For example, simulating protein folding may uncover new drug targets and inspire new approaches to treatment.

5. Creating new jobs and engines of economic growth

   Simulation technology has created substantial new employment opportunities and growth potential. According to the action plan aimed at advancing manufacturing digitalization issued by the Ministry of Industry and Information Technology, by 2025, the simulation-technology application coverage among key enterprises is expected to exceed 80%. With over 400,000 above-scale manufacturing enterprises nationwide, and with each enterprise needing at least two simulation engineers, the next two years could produce a talent gap approaching one million.

Simulation engineers’ salaries are also significantly above average. Statistics suggest that in 2025, the average monthly salary in Guangzhou’s simulation-technology industry reached 19.2K, about 64.1% higher than the city’s average wage. Salaries can reach 23,000 RMB/month for those with multidisciplinary expertise; engineers skilled in AI-accelerated simulation reportedly earn 40% more than traditional simulation engineers; and multi-physics simulation experts with 3–5 years of experience often earn 400,000–800,000 RMB per year.

 4.2 Negative Impacts: Challenges and Risks

However, the spread of “Simulation for Everything” also brings significant challenges and risks:

1. Cognitive confusion and neglect of reality

   As simulation advances, the boundary between virtual and real becomes increasingly blurred, potentially causing confusion in people’s understanding of reality. French philosopher Jean Baudrillard argued in Simulacra and Simulation that in modern society, simulacra have surpassed simple imitation and become independent existences that can even shape reality in return.

Over-immersion in virtual worlds can also cause people to overlook real-life problems and challenges. For special groups such as children, prolonged use of VR may affect real-world cognition. The Artificial Intelligence Ethics Subcommittee of the National Science and Technology Ethics Commission’s Ethical Guidelines for Virtual Reality Technology Research and Development notes that due to VR’s immersiveness, users’ moral emotions and moral will may be affected, potentially weakening or even disappearing.

2. Privacy and data-security risks

   Simulation models often require massive personal data to improve accuracy—biometrics, behavioral patterns, health information, and other sensitive data. If not properly protected, data can be leaked or abused.

Moreover, complex models and heavy data dependence make simulation systems vulnerable to cyberattacks. Attackers could exploit vulnerabilities to access, steal, or sabotage sensitive data, threatening individuals and organizations. In smart-city digital twins, insufficient security could allow hackers to obtain critical infrastructure information or even control traffic lights and power systems, causing severe consequences.

3. Misinformation and crises of trust

   Simulation also makes it easier to manufacture false information. Deepfakes and synthetic media can create highly realistic fake videos, audio, images, and texts—used to forge leaders’ speeches or spread fake news, eroding public trust, worsening political conditions, and intensifying social polarization and bias.

In legal contexts, simulation-based evidence may challenge authenticity verification. For example, evidence generated through simulation may be difficult to validate in court, creating risks for judicial fairness. A 2025 study by Stanford University highlighted AI-driven Counterfactual World Simulation Models (CWSM) in the legal field may raise ethical concerns, such as how to ensure the reconstructed scenarios are authentic while preventing the perpetuation of stereotypes during counterfactual simulations.

4. Ethical and social-norm issues

   Simulation creates ethical and normative challenges. First, it may blur definitions and boundaries of human identity—for example, the creation of multiple virtual avatars challenges traditional understandings of identity.

Second, responsibility and accountability in simulated environments can be unclear. Infringement or harm occurring in virtual spaces may lack a clear liable subject. For example, if one user’s avatar harms another user’s avatar in a VR game, should legal responsibility apply—and if so, who bears it? These issues require new ethical and legal frameworks.

5. Technological dependence and loss of autonomy

   Over-reliance on simulation may lead to loss of human autonomy and creativity. If all decisions depend on simulation outputs, human intuition and creativity may be ignored, resulting in homogenized decisions and insufficient innovation.

Such dependence can also lead to excessive trust in technology, even “technology worship.” Especially when AI and simulation are combined, blind trust may ignore errors and biases embedded in models, causing decision failures. In finance, for example, excessive reliance on AI simulation for investment decisions could amplify volatility and even trigger systemic risk.

 V. Differences and Connections Between “Simulation for Everything” and Virtual Reality

 5.1 Differences in Conceptual Meaning

“Simulation for Everything” and Virtual Reality (VR) are related but fundamentally different concepts.

“Simulation for Everything” is a cross-domain idea or technological methodology claiming that all things in the real world (entities, phenomena, systems) can be abstracted and modeled through technology for simulation. It spans many disciplines. Its core goals are “reproduce / predict / optimize”—using simulation to understand patterns, reduce real-world risk, and optimize system efficiency. It does not necessarily pursue “user experience.”

VR, in contrast, is a specific technology and interactive experience. It creates computer-generated 3D virtual environments and uses hardware (head-mounted displays, controllers, etc.) to produce an immersive sense of “being there.” Its core goal is “immersive experience,” constructing interactive virtual scenes so users feel presence—essentially sensory simulation centered on the user.

 5.2 Differences in Technical Scope

In terms of technical scope, “Simulation for Everything” includes all types of simulation technologies, while VR is only one branch within that broader set.

The technical scope of “Simulation for Everything” is extremely broad, encompassing all “simulation techniques,” such as digital twins (industrial simulation), computer simulation (climate/physics experiments), numerical Modeling (Financial Forecasting), VR/AR/MR (Immersive Experiences). 

It includes not only user-facing interactive simulation but also large amounts of back-end non-interactive simulation—for example, meteorological agencies simulating typhoon paths or scientists simulating galactic evolution.

VR is a branch of simulation technology with a more focused scope, centered on building immersion—3D modeling, real-time rendering, motion tracking, stereoscopic display, haptics/force feedback, etc.—and it relies on specialized hardware. VR also depends on active human interaction: users interact with the environment through headsets, controllers, movement, and viewpoint changes.

 5.3 Similarities and Differences in Application Scenarios

In application scenarios, the two both overlap and differ.

“Simulation for Everything” applies extremely broadly—industry (Equipment Operation and Maintenance Simulation), scientific research (particle-collision simulation), healthcare (pathology evolution simulation), social sciences (population growth simulation), philosophy (exploration of the nature of reality) and more. For example, in manufacturing, digital twins can simulate the full life cycle of products; in meteorology, supercomputers can simulate climate change; in economics, multi-agent models can simulate market dynamics.

VR is mainly used in scenarios requiring immersive experience: games and entertainment, education and training (e.g., VR surgical simulation), medical rehabilitation (e.g., VR therapy for acrophobia), and design visualization (e.g., VR architectural walkthroughs). For example, VR can create simulated operating-room environments for medical students to practice surgery; in real estate, VR can create virtual model homes so customers can experience space and layout.

 5.4 Relationship and Mutual Influence

Despite many differences, “Simulation for Everything” and VR are closely connected and mutually influential:

1. Technical support relationship: The idea of “Simulation for Everything” provides theoretical foundations and technical direction for VR, while VR is one important means to realize “Simulation for Everything.” VR development also pushes more advanced simulation algorithms and technologies—more realistic physics, more efficient rendering, etc.

2. Complementary application relationship: In many scenarios, the two can be used together. In urban planning, planners can first use non-interactive simulation to model development trends, then use VR to allow planners and citizens to immerse themselves in plans and provide feedback.

3. Mutual promotion relationship: The “Simulation for Everything” idea drives VR development, while VR maturity enables broader simulation applications. As VR advances, more complex systems can be interacted with and controlled through VR interfaces, expanding simulation’s application boundaries.

 VI. Future Development Trends of “Simulation for Everything”

 6.1 Technological Convergence and Innovation

In the future, “Simulation for Everything” will continue to drive technological convergence and innovation:

1. Multi-technology convergence: AI, big data, cloud computing, IoT, quantum computing, and simulation will deeply integrate to form stronger simulation capabilities. AI + simulation will make simulation more intelligent—automatically optimizing parameters, recognizing patterns, and forecasting trends. Quantum + simulation will dramatically increase compute power, enabling simulation of more complex systems.

2. Wider adoption of digital twins: Digital twins will expand from industry into broader domains such as cities, ecosystems, and the human body. Forecasts suggest the global digital twin market will reach $52.3 billion by 2030, with China growing faster than the global average (projected CAGR 16.3%) and reaching 34% of the global market ($17.8 billion) by 2030.

3. AI-driven intelligent simulation: AI will play an increasingly important role in simulation—automatic model construction, parameter optimization, result analysis, etc. Platforms like ANSYS SimAI exemplify this by cutting computation time from hours to minutes.

4. Breakthroughs in quantum simulation: Quantum computing will achieve disruptive applications in molecular simulation, materials design, and drug R&D. Forecasts suggest that by 2035, quantum computing could generate $877 billion in economic impact, with simulation applications contributing significantly.

 6.2 Expansion of Application Domains

The applications of “Simulation for Everything” will continue expanding beyond traditional industrial and military uses:

1. Smart cities: Digital twins will play larger roles in planning, traffic management, and resource allocation. For example, the “Simulation Ningxia and AI Simulation Open Source” platform has applied AI simulation at scale to urban governance, offering intelligent solutions for flooding, gas leaks, forest fires, and other challenges.

2. Smart healthcare: Simulation will support diagnosis, surgery planning, and drug discovery. Patient-specific organ digital twins can enable surgeons to rehearse procedures virtually and optimize plans; molecular dynamics can predict binding energy and bioactivity to accelerate drug development.

3. Smart agriculture: Simulation will support crop growth modeling, pest/disease forecasting, and resource optimization. Crop models can predict yield under different climates and inform decisions; pest-spread simulations can guide more effective control strategies.

4. Smart energy: Simulation will support energy-system optimization, renewable energy development, and energy-network management. Power-system models can simulate power flow under different loads to optimize grid structure and operations; wind-farm airflow simulations can optimize turbine layouts and improve wind utilization efficiency.

 6.3 Ethics and Regulation

As “Simulation for Everything” becomes more widespread, ethics and regulation will gain increasing attention:

1. Building ethical frameworks: Countries will accelerate development of ethical frameworks to guide simulation R&D and application. For example, in April 2025, China’s Ministry of Science and Technology issued the Ethical Guidelines for R&D in Virtual Reality Technologies, providing ethics-review guidance for VR R&D, basic medical research, disease modeling, drug toxicity assessment, and drug screening.

2. Improving regulatory mechanisms: Countries will establish stronger regulatory systems to ensure safe, reliable, and compliant simulation use. Laws and regulations on data security, privacy, and intellectual property will continue to improve, providing safeguards for healthy development.

3. Developing industry standards: Industry organizations and standards bodies will accelerate standard-setting to promote normalization. In digital twins, for example, International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC) are developing standards to define digital twin concepts, architectures, and applications.

4. Strengthening public education: Public education efforts will expand to improve understanding of simulation technologies, enhance discernment, and strengthen safety awareness—through science outreach, media communication, and other approaches—so the public can use simulation correctly.

 VII. Conclusion: The Dialectical Relationship Between Simulation and Reality

The rise and development of the “Simulation for Everything” idea deeply reflects humanity’s exploration of the world’s essence and its pursuit of technological capability. From philosophical speculation to technical realization, this idea has become an important force driving scientific and social progress.

On the one hand, “Simulation for Everything” promotes technological innovation and application, advances scientific research, improves the scientific basis and accuracy of decisions, reduces cost and risk, and creates new employment opportunities and growth engines. On the other hand, it also brings challenges and risks in cognition, privacy and security, misinformation, ethics, and social norms.

Looking ahead, as technology advances and applications expand, “Simulation for Everything” will continue to profoundly influence human society. A dialectical attitude is required: we should fully harness simulation’s positive effects to promote scientific and technological advancement and social development, while also taking its risks seriously—using ethics, legal regulation, and technological innovation to ensure simulation develops healthily and is applied safely.

“Simulation for Everything” does not mean the dissolution of reality; it means enhanced human capability to understand and transform reality. In this process, we must maintain respect for reality and a rational attitude toward technology, finding a balance between the virtual and the real, and jointly building a smarter, safer, more sustainable community with a shared future for humankind.

We look forward to the arrival of a new era of “universal simulation.”

— Ai Yawen (艾亚文)