SCIENTIFIC MACHINE LEARNING

Science.
In sync.

AI R&D for science and engineering.

We study AI that brings physical laws and observations together. Our focus spans agents that connect research tools, PINN modeling, 3D reconstruction, and digital twins.

Explore our technology

[ SYNCPEAR / SCIENTIFIC AI ]

PHYSICS × DATA × INTELLIGENCE01 / ∞

From scientific questions to real-world systems.Explore below

AI AGENTSSciML & PINNs3D RECONSTRUCTIONDIGITAL TWINS

01 / OUR TECHNOLOGY

The problem.
The method.

Before choosing a method, we look at the problem and the available material. These are topics for discussion and evaluation criteria across our technology areas.

T / 01AI AGENTS

AI that connects research tools.

Finding information, running code, comparing experimental conditions, and organizing results. We explore research agents that connect these steps in scientific and engineering workflows.

Problems to discuss: Repeated searches for experimental conditions, running analysis tools, and organizing results
Useful inputs: Technical documentation, runnable code, tool interfaces, and workflow descriptions
What to evaluate: Supporting evidence, tool outputs, human review points, and reproducibility

REASONING / TOOLS / EXPERIMENTSHow we assess a project

T / 02SciML / PINNs

Model with physics and data.

Physics-informed neural networks (PINNs) incorporate physical equations and initial and boundary conditions into training, alongside observations when available. We consider problems such as state estimation and inverse estimation of material properties.

Problems to discuss: Estimating states or parameters from partial observations, and predicting physical behavior
Useful inputs: Governing equations, initial and boundary conditions, units, and available observations
What to evaluate: Error against reference analyses or experiments, equation residuals, and limits outside the training range

PHYSICS / DATA / LEARNINGData preprocessing example PINN resources · external

T / 033D / DIGITAL TWINS

From shape to changing states.

3D reconstruction recovers shape from images and observations. Digital twins connect a model with data from a physical counterpart and update it to explore states and changes.

Problems to discuss: Recovering object or space geometry, and comparing states by connecting observations with a model
Useful inputs: Multi-view images or video, camera and sensor information, and operating conditions
What to evaluate: Geometry and scale accuracy, agreement with observations, and data update conditions

RECONSTRUCTION / SIMULATIONDiscuss 3D or digital twins About digital twins · external

02 / UNDERSTAND YOUR DATA

Before the model,
understand the data.

Reducing noise can also remove meaningful changes. Adjust the moving-average window to see how preprocessing changes the same observations.

Modeling also calls for checking sampling intervals, missing values, units, and observation conditions.

An educational data preprocessing example using a synthetic time series.

SIGNAL STUDY / 001

ObservationsMoving average

0SAMPLES / 128127

Moving-average window9 observations

A wider window reduces noise, but can also flatten short-term changes.

03 / OUR APPROACH

Define the problem.
Define the evidence.

We examine the data, physical conditions, and intended use before choosing a method. Development scope and evaluation criteria are discussed for each project.

Set the problem and the baseline.

Clarify what to predict, which parameters to estimate, or which tools to connect. Review available material and the working environment, then define a reference for comparison.

Start with a bounded scope.

Consider methods in light of data quantity, quality, and physical conditions. Begin with a scope that can be compared against reference analyses or experiments to examine feasibility and limits.

Check results and conditions of use.

Review error, runtime, reproducibility, and failure conditions together. Identify the model's valid range, data update needs, and remaining validation work.

04 / ABOUT SYNCPEAR

SCIENCE. INTELLIGENCE. REALITY.

From science.
To reality.

SyncPear explores AI agents, physics-based modeling, 3D reconstruction, and digital twins through scientific machine learning (SciML).

We bring research tools, constraints from physical equations, and observations of physical systems into the same discussion. Our starting point is finding a model suited to the problem and a meaningful way to evaluate it.

Discuss a project

05 / START A CONVERSATION

Tell us what
you want to solve.

Start with the problem you want to solve. We discuss projects around the scope of the problem and the material you have today.

The problem and your current difficulties
Available data, code, and analysis tools
Expected outcomes and a reference for comparison
Your timeline and the scope of collaboration

hello@syncpear.com

Science.In sync.

The problem.The method.