Home

ModelBased

Model-based refers to approaches that rely on an explicit model of a system, its environment, or its behavior in order to reason, simulate, design, or control it. The model serves as a primary instrument for prediction, verification, and decision-making, rather than relying solely on observed data or ad hoc rules.

Models can be mathematical, logical, or data-driven representations. They may describe dynamics over time (differential equations,

Model-based methods are widely used in engineering and software development. Model-based systems engineering (MBSE) formalizes system

Methods and lifecycle: Build, validate, and maintain models; use simulations to explore behavior; integrate with real

History and scope: The use of explicit models in engineering traces back to control theory and systems

Challenges and advantages: Benefits include earlier verification, reduced development risk, and improved traceability between requirements and

state
machines),
structure
(architecture
diagrams,
graphs),
or
probabilistic
behavior
(stochastic
processes).
In
MBSE
and
related
disciplines,
the
model
is
used
throughout
development
to
capture
requirements,
architecture,
and
behavior.
design
around
comprehensive
models.
In
control
and
robotics,
model-predictive
control
and
state
estimation
rely
on
models
to
predict
future
states
and
optimize
control
actions.
In
software
engineering,
model-based
testing
and
model-based
generation
of
artifacts
automate
test
cases
and
code.
data
via
estimation
techniques
like
Kalman
filters;
apply
optimization
strategies
such
as
model-predictive
control
(MPC).
Models
are
iteratively
refined
as
more
data
becomes
available
and
requirements
evolve.
engineering;
the
term
gained
prominence
with
model-based
design
in
aerospace
and
automotive
industries
in
the
late
20th
century
and
later
with
MBSE
frameworks
and
tools
(e.g.,
SysML,
Simulink).
implementation.
Limitations
include
the
effort
to
create
and
maintain
accurate
models,
potential
model
misspecification,
and
computational
demands
for
complex
simulations.