Most teams still run simulations to check what one design will do.
But product decisions aren’t made one design at a time, they’re made across trade-offs, timelines, and constraints.
The missing piece has been fast, trustworthy data to illuminate the entire design space, not just a handful of points.
Today, we’re changing that.
The problem we kept hearing
Traditional tools tell you what a design will do, not what your best designs might be. That distinction matters.
When you can only sample a few candidates, you end up choosing “good enough,” and it’s hard to know whether a better option was just out of sight.
You feel that risk as uncertainty about whether you actually chose the best feasible design under real constraints (manufacturing tolerances, compliance, deadlines).
Across industries, we kept hearing the same story.
Teams wanted to bring AI into the loop, but assembling a high-quality, strongly coupled multiphysics dataset took weeks or months — if it came together at all.
Without the right data, AI stayed a slide, not a tool.
Meanwhile, the underlying issue with the decision-making still persists: with limited visibility, you’re trying to find the best solution without seeing all the options.
Introducing Quanscient MultiphysicsAI
Quanscient MultiphysicsAI is our answer.
It combines two things our customers already trust us for — natively coupled multiphysics and cloud-scale throughput — with a decision-first AI workflow.
- Create proprietary training data
Run up to millions of natively coupled simulations in parallel with Quanscient Allsolve to produce multiphysics datasets tailored to your product. - Train physics-aware neural networks
Use that data to train a surrogate that can make hundreds of thousands performance predictions in milliseconds. - Decide with full design-space visibility.
The fast model reveals the design landscape, surfaces the few best trade-offs, and shows which options meet your goals—instantly. - Verify before you choose
Validate the top options with Allsolve so every decision is grounded in physics.
| Quick definition: A Pareto front is the set of “best-possible” designs where improving one objective would worsen another (e.g., bandwidth vs. sensitivity, efficiency vs. weight). Seeing the front means you’re choosing among the true trade-offs, not guessing. |
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This is an example of a Pareto front for a car engine: the set of best possible designs balancing two (or more) competing objectives.
An example from the real world
To make this concrete, consider a recent PMUT study we ran with Allsolve and MultiphysicsAI.
We began with the specs and boundaries the team had to respect.
Instead of inching through geometry tweaks, we generated a proper dataset: roughly ten thousand high-fidelity, natively coupled simulations—about five seconds each—spanning geometry variants and four KPIs.
On top of that data, we trained a surrogate in about ten minutes. The team could then evaluate candidates in milliseconds and, critically, see where the model’s confidence was high and where it wasn’t. At 12 MHz, the key decision hinged on bandwidth versus sensitivity.
The Pareto front made the best combinations obvious: the points where you can’t improve one without hurting the other.
We promoted the finalists back to Allsolve and re-simulated at full fidelity. The results held.
The Pareto front clearly shows the set of best possible designs, with validation simulations confirming them. It's easy to see how the original design was improved.
Fractional bandwidth improved from 65% to 100%, and sensitivity increased by 6.5%. These weren’t just pretty plots, they were actually feasible designs ready to proceed.
That’s what MultiphysicsAI is for: a faster path to better engineering decisions, verified.
Why this is different
Plenty of teams are experimenting with “AI for engineering.” The difference here is that the physics does the heavy lifting first.
Allsolve generates proprietary, high-fidelity, natively coupled data at cloud scale, so the surrogate isn’t guessing in the dark—it’s learning from the right signals.
That foundation makes the model fast and honest: you get millisecond predictions along with accuracy metrics and confidence bands, so you can see where it’s solid and where it needs another look.
The second difference is intent. This isn’t another black box that promises magic and leaves you to reconcile the result.
MultiphysicsAI is built for decisions. It shows the real trade-offs through Pareto fronts, lets you work backward from targets with inverse design, and then sends finalists back to Allsolve for ground-truth verification.
You move quickly when the model is confident, and you slow down by design when the data says to verify.
That’s how velocity and trust can coexist in the same workflow.
Here is how you can get started now
If you’re curious what physics-aware AI could enable for you—whether you’re an Allsolve user or not—the first step is simple: fill in the form on our MultiphysicsAI webpage and we’ll schedule a short discovery call.
On that call, we’ll walk through:
- How MultiphysicsAI could help with your product development processes
- The practical results you could realistically expect with physics-aware AI
- Simple next steps and recommendations tailored to your team
No commitments on that first call—just a clear view of how physics-aware AI could give you an advantage today.
Closing words
For years, simulation has told us whether one idea might work.
What product teams need is the confidence that they’re choosing the best feasible idea among many—and that they can defend that choice to leadership, suppliers, and customers.
That’s why we built MultiphysicsAI: physics to generate the right data, AI to illuminate the design space, and physics again to verify the winners.
If this is the moment to see your full design space, I’d like to talk.
Get in touch now to see what physics-aware AI could do for you -->
