Introduction
Engineering has always been about translating abstract goals into real-world performance. Whether the task is to design a more efficient motor, a more sensitive ultrasonic sensor, or a lighter aircraft structure, engineers are essentially solving inverse problems: they know the target behavior they want, but not the exact design that will achieve it.
Traditional simulation tools were built to answer a different question, “What happens if I build this design?” They model behavior, not discovery. As a result, engineers are forced into slow, iterative loops of prototyping, testing, and adjustment. Even with advanced finite-element solvers, a single high-fidelity simulation can take hours or days. Exploring thousands of possible designs becomes impractical.
This is where MultiphysicsAI changes the equation.
Allsolve generates the multiphysics simulation data, AI allows us to rapidly explore the design space to discover new designs, rapidly, accurately, and at scale. By combining the numerical rigor of physics-based simulation with the pattern-recognition power of artificial intelligence, MultiphysicsAI doesn’t just model existing designs, it creates the data needed to discover new ones.
With this capability, engineers can invert their workflow, starting with the desired outcomes and letting MultiphysicsAI identify the designs that make them possible. In other words, we provide the foundation for intelligent, data-driven discovery in engineering design.
This concept is made real through Quanscient Allsolve, a cloud-based multiphysics simulation software that enables everything described above. Quanscient Allsolve doesn’t just run simulations, it creates the high-quality training data that powers MultiphysicsAI. This allows us to train AI models to explore the design space, solve inverse problems rapidly.
By learning from its own simulation results, Quanscient Allsolve becomes a self-reinforcing system, every simulation refines the AI’s understanding, allowing it to explore the design space in minutes instead of days. This allows engineers to start from desired outcomes and quickly explore the design space with reliable, physics-informed data.
In this way, Quanscient Allsolve transforms simulation from a bottleneck into a continuous source of insight, accelerating discovery and making intelligent design exploration a practical reality.
The power of the model
The central idea of MultiphysicsAI is simple but powerful.
Use physics-accurate simulations to generate data, and train an AI model to understand the full relationship between design variables and performance outcomes.
Once trained, the AI becomes a surrogate for the original solver, predicting the results of new designs instantly and with high accuracy.
Speed and scale
A typical finite-element may require minutes or hours per run. A trained MultiphysicsAI model executes predictions at over 100,000 evaluations per second. That acceleration enables engineers to explore enormous design spaces that were previously unreachable.
In practical terms, what once required 100,000 simulations and months of computation can now be approximated in a few seconds. This opens the door to true design-space exploration, statistical robustness studies, and rapid optimization.
Multi-objective insight
Most real engineering problems involve trade-offs, for example, between sensitivity and bandwidth in an ultrasonic transducer, or between stiffness and weight in a composite structure. MultiphysicsAI naturally supports multi-objective optimization.
By sweeping through the AI-predicted design space, we can compute Pareto fronts, sets of designs that represent the best achievable trade-offs between objectives. Instead of a single “best” design, engineers get a portfolio of optimal candidates, each emphasizing different priorities.
Bridging simulation and experiment
AI models also bridge the gap between simulation and physical measurement. Once trained on virtual data, they can process experimental inputs, such as impedance spectra, vibration modes, or thermal signatures, and infer underlying material properties or hidden parameters. This creates an integrated feedback loop between modeling and lab testing.
Current status and what we need more
Despite its promise, MultiphysicsAI is not a replacement for physics solvers; it is an accelerator and amplifier of their value. The current generation of models is built on classical finite-element methods (FEM) running at scale in the cloud. These provide the ground truth for training datasets, typically consisting of tens of thousands of simulated designs.
Yet, there are several frontiers still being explored:
- Data fidelity and mesh resolution
Training data quality directly limits AI accuracy. Re-running simulations with finer meshes or higher-order elements improves the learning base but increases computational cost. Efficient sampling strategies and adaptive meshing remain active areas of development. - Loss mechanisms and parasitics
Many models currently neglect secondary effects such as dielectric loss, parasitic capacitances, or process-induced defects. Including these in the simulation and training process is key for predictive reliability in real manufacturing contexts. - Generalization to new geometries and materials
Most models are trained for specific device families, disks, membranes, cantilevers, etc. Extending them to arbitrary geometries or heterogeneous materials will require both broader datasets and more general neural architectures. - Integration with experimental pipelines
The ultimate goal is closed-loop design: measurements feed directly into updated AI models, which then suggest refined designs for the next fabrication run. This integration is still in its early stages. - Quantum-accelerated simulation
Quanscient is leading research into quantum multiphysics solvers, which will eventually generate training data exponentially faster.
Quantum algorithms can handle larger coupled systems and non-linearities beyond classical scaling limits. Once mature, they will make truly general AI design models feasible.
Case examples
Pareto-based design
In engineering, achieving the best design often means balancing competing goals, for example, maximizing efficiency while keeping costs low, or increasing sensitivity without sacrificing bandwidth.
The idea of an optimal balance comes from what researchers call the “Pareto front”, the set of designs where improving one aspect would inevitably worsen another. Understanding and visualizing this balance helps engineers identify the most efficient trade-offs instead of chasing a single “perfect” design.
From optimization to understanding
Traditional optimization techniques require engineers to define a single objective function, a mathematical formula that combines multiple targets into one. The choice of weights or coefficients in that formula is often arbitrary, meaning two engineers might define entirely different “optimal” designs.
MultiphysicsAI takes a more transparent approach. Instead of searching for one solution, it shows all the best possible ones. The AI model, trained on thousands of simulations, predicts the performance of millions of design combinations in seconds. From these predictions, the Pareto front is constructed, a clear visual map of what trade-offs exist and how to navigate them.
This makes the process not only faster but also more explainable. Engineering teams can see why a design is optimal and choose the variant that best fits their product, cost, or manufacturing priorities.
Real-world impact
Pareto-based analysis is widely applicable, from micromechanical devices to engines, optical components, and beyond. Once the AI model is trained, generating new Pareto fronts takes seconds, not weeks. This fundamentally changes how teams approach design: instead of guessing the right trade-offs, they can explore them interactively.
PMUT design optimization
PMUTs (Piezoelectric Micromachined Ultrasonic Transducers) are miniature ultrasonic devices used in medical imaging, fingerprint sensing, gesture recognition, and industrial monitoring. Their design involves multiple coupled physical effects, structural mechanics, acoustics, and electrostatics, making them ideal candidates for MultiphysicsAI.
The design challenge
A PMUT consists of a multilayer membrane suspended over a cavity. Key geometric parameters include:
- Elastic layer thickness
- Piezoelectric layer thickness
- Cavity radius
- Bottom-electrode radius
Engineers aim to optimize several performance metrics:
- Transmit sensitivity (Pa/V)
- Center frequency (Hz)
- Fractional bandwidth (%)
- Impedance at resonance (Ω)
Sensitivity and bandwidth, in particular, tend to trade off, increasing one typically reduces the other.
Data generation and AI training
Using Quanscient Allsolve, we ran 10,000 PMUT simulations, each with randomized geometry. Each simulation took about five seconds and computed all four KPIs.
After analyzing the correlations between design variables and performance, an AI model was trained to act as a forward predictor, taking geometry as input and outputting the KPIs.
Training took around 10 minutes on a GPU, resulting in an accuracy better than 1% and execution time under a millisecond.
Optimization and results
Once trained, the AI model was used to compute the Pareto front between sensitivity and bandwidth.
This allowed us to explore a wide range of optimal trade-offs and quickly identify the best-performing designs.
For example, it enabled us to select a design with a 35% improvement in fractional bandwidth and a 6.5% improvement in sensitivity compared to the original configuration.
Many other combinations along the Pareto front are available, depending on the specific performance priorities. Furthermore, frequency constraints (e.g., maintaining a 12 MHz center frequency) could be easily applied, letting designers focus on relevant operating ranges.
Because the AI executes so quickly, this exploration process takes seconds, turning an inherently complex optimization into an interactive experience.
Piezoelectric material characterization
Understanding and characterizing piezoelectric materials is essential for building reliable actuators and sensors. Yet, conventional testing methods require intricate setups and can be time-consuming.
Approach
Quanscient Allsolve simulated 10,000 piezoelectric discs with randomized material constants and geometries. The solver computed the electrical impedance spectrum, magnitude and phase, for each disc.
These spectra served as the input data, while the true material parameters (stiffness, piezoelectric, and permittivity matrices) were the target outputs for training an inverse AI model.
The trained model could then infer the material properties of a real disc simply from its measured impedance and geometry, without mechanical testing.
Benefits and extensions
This method enables fast, non-destructive characterization of piezo materials and can easily be extended to:
- Thin-film MEMS samples
- Lateral or shear-mode devices
- More complex composite structures.
Next steps include incorporating dielectric losses, improving mesh fidelity, and validating predictions with experimental data.
What Quanscient Allsolve provides?
Quanscient Allsolve is a cloud-based, multiphysics platform for both simulation and AI training.
It delivers:
- >100× speedup over conventional FEM tools
- Seamless scaling across cloud hardware
- The ability to generate large, randomized training datasets
Quanscient Allsolve serves as both the solver and the data engine powering MultiphysicsAI. Built on top of Quanscient Allsolve, MultiphysicsAI demonstrates how AI can become an integral part of engineering workflows. It enables:
- Rapid AI model training (typically under 10 minutes)
- Surrogate models achieving <1% error
- Instant inference (<10 mss per prediction)
- Automatic generation of Pareto fronts and sensitivity analyses
Together, these capabilities show how AI-driven exploration can move from research to practical, everyday use.
Quantum multiphysics roadmap
Quanscient is also developing quantum-accelerated solvers, capable of handling exponentially larger and more complex simulations.
Quantum multiphysics will enable:
- Faster generation of training data
- Modeling of non-linear and stochastic systems
- Training of truly general MultiphysicsAI models
This marks the next frontier in combining simulation, AI, and quantum computation into one coherent framework.
Conclusion
Engineering is moving from analysis to intelligence. Where once we simulated individual designs, we can now model entire design spaces. MultiphysicsAI transforms simulation results into an engine of discovery, turning data into understanding and understanding into design.
The benefits are tangible:
- Orders-of-magnitude speed improvement
- Clear visualization of trade-offs
- Reduced reliance on trial-and-error
- Seamless integration between modeling and experiment
MultiphysicsAI does not replace engineers, it empowers them. It provides the insight to see every feasible design and the freedom to choose the one that best meets their goals.
Through MultiphysicsAI, and quantum multiphysics, our goal is simple: to make complexity a creative advantage. When computation and physics converge, the question is no longer “what will this design do?” but “what design should we create?”
Learn more about Quanscient and get in touch now at quanscient.com
