represents an exponential function.
ExponentialModel[vars]
uses explicit variable specification vars.
ExponentialModel[pars,vars]
uses the provided coefficients pars.
ExponentialModel
represents an exponential function.
ExponentialModel[vars]
uses explicit variable specification vars.
ExponentialModel[pars,vars]
uses the provided coefficients pars.
Details
- ExponentialModel represents an exponential in the given variables in a format suitable for symbolic or numerical evaluation and fitting.
- Exponential models describe processes where change is proportional to the current state, such as growth, decay, relaxation and approach to equilibrium.
- Single-variate ExponentialModel is parametrized as
. - Multivariate exponentials are parametrized as
. - When not specified, variables will automatically be enumerated using x[i].
- Valid variable specifications vars include:
-
n the number of variables symb a symbolic representation of a single variable {symb1,…} a list of symbolic variables - When not specified, parameters will automatically be enumerated using C[i].
- Valid parameter pars specifications in the form {par1,…} include:
-
val a fixed parameter value val par a symbolic parameter name par parval a symbolic name par set to a fixed value val {par,val0} a symbolic parameter name par with the initial value val0 - Model properties can be extracted using Information[PowerModel[…],prop].
- Valid basic properties include:
-
"BaseType" model base type "Name" model name "ShortName" short identifier to use as label "InputType" supported input types "OutputType" supported output types - Valid data-related properties include:
-
"ColumnNames" names of the input features "ColumnVariableMap" map between column names and model variables "InputSize" dimensionality of the input "OutputSize" dimensionality of the output "Trainable" whether the model is fully specified and can be trained "Trained" whether the model can be evaluated numerically "VariableColumnMap" map between model variables and column names "Variables" name of the model variables - Best model-related properties include:
-
"Expression" model expression "Function" model as a pure function "SymbolicExpression" model expression with symbolic parameters "TabularFunction" pure function suitable to work on a tabular row - Parameter-related properties include:
-
"ParameterAssociation" association of parameter names and values "ParameterCount" the number of parameters "ParameterInitialValues" initial values for the fit "ParameterNames" parameter names "ParameterRules" list of rules with parameter names and values "Parameters" parameter values if present; names otherwise "ParameterValues" parameter values "Constraints" parameter constraints
Variables
Parameters
Properties
Examples
open all close allBasic Examples (3)
Specify a generic exponential model:
ExponentialModel[]Use an explicit input size and custom names for the symbolic parameters:
ExponentialModel[{a, b, c}, 1]Plot over a variety of growth factors using constant parameters:
Plot[{ExponentialModel[{0, 1, 1}, 1][{x}], ExponentialModel[{1, 1, -1}, 1][{x}], ExponentialModel[{-0.5, 1, .5}, 1][{x}]}, {x, -1, 1}]Scope (22)
Variables (6)
ExponentialModel[]The number of variables is inferred from the arguments:
ExponentialModel[][{x}]
ExponentialModel[][{x, y, z}]ModelFit will assume the number of variables is one less than the dimensionality of data points:
ModelFit[{{0, 0, 0}, {1, 9, 55}, {1, 1, 7}, {1, 4, 25}, {8, 9, 118}, {8, 10, 124}, {8, 5, 94}}, ExponentialModel[]]Specify the number of variables:
ExponentialModel[2]Give the variables a custom symbolic representation:
ExponentialModel[{var1, var2}]These names are overwritten when the model is evaluated:
ExponentialModel[{var1, var2}][{a, b}]Visualize a multivariate exponential model:
Plot3D[Evaluate@ExponentialModel[{0, 1, -0.5, .4}, 2][{x, y}], {x, -1, 1}, {y, -1, 1}, ColorFunction -> "Rainbow"]Parameters (4)
Parameter names are assigned automatically:
ExponentialModel[2]Specify custom parameter names:
model = ExponentialModel[ {c, A, k}, 1]Set a parameter to a specific value:
model = ExponentialModel[{0, 2, k}, 1]Specify both parameter names and values:
model = ExponentialModel[{c, a -> 2, k}, 1]The ordering matches the automatic enumeration of the C[n]:
ExponentialModel[][x]Evaluation (5)
Symbolically evaluate a single-variable model:
ExponentialModel[1][x]Symbolically evaluate a two-variable model:
ExponentialModel[2][{x, y}]The number of variables is automatically inferred if not specified:
ExponentialModel[][x]ExponentialModel[][{x, y}]Evaluate the model on multiple symbolic variables:
ExponentialModel[2][{{x, y}, {a, b}}]Evaluate the model on a list of points:
ExponentialModel[1][{{1}, {2}, {3}}]Information (5)
View general information about a model:
Information[ExponentialModel[]]Some information is only available when variables or parameters are fully specified:
Information[ExponentialModel[{x, y}]]Information[ExponentialModel[{a, b, c}, x], "Variables"]Information[ExponentialModel[{a, b, c}, x], {"Variables", "Parameters"}]Get information about the default model values:
Information[ExponentialModel[1], {"Variables", "Parameters"}]Applications (9)
Basic Applications (8)
Voltage across a capacitor approaches the supply voltage exponentially when charging through a resistor:
model = ModelFit[data -> "Voltage", ExponentialModel[]]model = ModelFit[Tabular[Association["RawSchema" -> Association["ColumnProperties" ->
Association["Time" -> Association["ElementType" -> "Real64"],
"Voltage" -> Association["ElementType" -> "Real64"]], "KeyColumns" -> None,
"Backend" -> "WolframKernel"], "Options" -> {},
"BackendData" -> Association["ColumnData" -> DataStructure["ColumnTable",
{{TabularColumn[Association["Data" -> {{5.838988328758781, 6.738191996308977,
6.19608830911937, 5.80789503296751, 6.250604171731046, 5.179844714242305,
8.721810088941064, 1.7205702996647743, 8.577864810423153, 0.7222558265533063,
4.338023011638031, 6.7412711375946515, 1.9644724536929292, 5.046552471939199,
0.9969698240844305, 4.337188925837601, 7.020937526546442, 8.335043162393749,
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6.8587291812564555, 2.8433778568332735, 8.34314308472577, 1.8803062830582484,
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5.285372338196844, 1.1778497669069798, 1.559562748897858, 3.6811765405561103,
0.6636163376087456, 9.374473666847713, 2.4112841981970567, 2.8381854361673264,
8.058768704225601, 7.697273935110871, 2.047669915806134, 2.9506849572499427,
5.081294282256998, 7.069862276305976, 4.772907835970401, 6.676727517173527,
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4.975208740425168, 8.661731830268737, 1.6964602183192867, 2.6692931146107646,
6.4338767780326815, 4.868275843700294, 5.559284856045961, 6.068822400277101,
4.77948482866273, 5.639648265365826, 0.10472243170756235, 5.948881449956518,
2.9549286836210653, 8.383000763138773, 6.2399090997234445, 4.660007510168757,
0.9996706653056076, 5.994272170067719, 9.246351546390024, 1.8966964723831725,
0.475840391126654, 8.54632535772652, 4.560884513302382, 5.387913714615083,
6.302598648702816, 9.958759058383801, 1.7813742791318932, 7.316117239291017,
2.813291724138469, 9.809682227983348, 7.708060380700297, 9.028271919774538,
9.47123012552268, 7.095565769183219, 7.36683153755493, 2.467582202007166,
6.864612386346513, 5.0949618589117}, {}, None}, "ElementType" -> "Real64"]],
TabularColumn[Association["Data" -> {{4.6966608878120155, 4.824312283883162,
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4.5503630336586625, 4.676774078885084, 0.2779261670640416, 4.725688518479375,
3.9127058431000403, 4.912771073391018, 4.706956433628692, 4.368940658754038,
1.9889028914188887, 4.757798616779952, 4.848641841535179, 3.011532716133896,
1.0434839585621511, 4.94165839642236, 4.470539925798977, 4.595770638864039,
4.719122945110612, 4.927458297680816, 2.954987997076547, 4.875258039312025,
3.832285369585581, 4.90716312590821, 4.886081978517763, 5.014719857724921,
4.995918853828975, 4.8432863841043785, 4.978770965631156, 3.565109756764496,
4.814056522876913, 4.662826256104416}, {}, None}, "ElementType" -> "Real64"]]}}]]]] -> "Voltage", ExponentialModel[]]Compare with the symbolic result:
DSolveValue[{v'[t] == (V - v[t]) / (R C), v[0] == 0}, v[t], t]//SimplifyCurrent through an inductor increases exponentially after a voltage step:
ModelFit[Tabular[Association["RawSchema" -> Association["ColumnProperties" ->
Association["Time" -> Association["ElementType" -> "Real64"],
"Current" -> Association["ElementType" -> "Real64"]], "KeyColumns" -> None,
"Backend" -> "WolframKernel"], "Options" -> {},
"BackendData" -> Association["ColumnData" -> DataStructure["ColumnTable",
{{TabularColumn[Association["Data" -> {{2.212919562876545, 1.1561178651027557,
9.661722821647654, 6.826158647065498, 8.467111066389252, 11.145589431454844,
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2.678984329416374, 4.154218756176009, 2.0797307235947065, 11.591582737904774,
3.836199061728779, 10.294469622826554, 5.146326790249952, 2.6572834034560007,
1.5110411278935683, 7.8585557765629055, 10.816602932120025, 11.514701763813106,
4.749624619990653, 2.8522849238016352, 11.560984001295422, 10.068374960966082,
1.3355721532621718, 5.044639825120428, 6.726474733164955, 10.148747709592602,
5.611651234603483, 7.0168978218759595, 6.414187894270681, 1.4376410136000457,
3.6664733145346844, 10.053652863953754, 5.590928658805095, 9.390482036277543,
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4.582117504682287, 10.393489202778664}, {}, None}, "ElementType" -> "Real64"]],
TabularColumn[Association["Data" -> {{1.0665008742997244, 0.6386618092410143,
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0.695508691588814, 1.6162550464295729, 1.812447513633879, 1.932406718536364,
1.7382107669962683, 1.807284572204545, 1.7343703250707732, 0.7269834339065654,
1.4111610222679576, 2.0188002854874196, 1.6652293961390294, 1.9521817654946807,
1.4915165887846673, 1.509758908636861, 1.9908931952073223, 1.9572957214845867,
1.593302883046879, 1.9105412685144856}, {}, None}, "ElementType" -> "Real64"]]}}]]]] -> "Current", ExponentialModel[]]Object temperature relaxing to ambient temperature (Newton's law of cooling):
ModelFit[Tabular[Association["RawSchema" -> Association["ColumnProperties" ->
Association["Time" -> Association["ElementType" -> "Real64"],
"Temperature" -> Association["ElementType" -> "Real64"]], "KeyColumns" -> None,
"Backend" -> "WolframKernel"], "Options" -> {},
"BackendData" -> Association["ColumnData" -> DataStructure["ColumnTable",
{{TabularColumn[Association["Data" -> {{1.285645967204907, 1.8712787246468885,
11.204913120079635, 0.9327675678511369, 15.425846238297659, 2.9432446478631284,
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6.025896132194127, 3.95526363138357}, {}, None}, "ElementType" -> "Real64"]],
TabularColumn[Association["Data" -> {{78.05020850105669, 70.8225882348392,
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30.208350984038194, 27.491179054506325, 73.13020074022178, 21.944446544272402,
24.661303328092785, 98.6251899480942, 26.406881471436087, 24.062356153732413,
46.57404944608977, 42.025084404469574, 39.89434722507042, 32.60072888558425,
23.776124586255563, 30.168343795784224, 26.20609727030415, 30.85340262489411,
37.47373198632847, 49.36871094213025}, {}, None}, "ElementType" -> "Real64"]]}}]]]] -> "Temperature", ExponentialModel[]]DSolveValue[{T'[t] == -(T[t] - Tenv) k, T[0] == T0}, T[t], t]//FullSimplifyRadioactive decay with constant decay rate:
ModelFit[Tabular[Association["RawSchema" -> Association["ColumnProperties" ->
Association["Time" -> Association["ElementType" -> "Real64"],
"Count" -> Association["ElementType" -> "Integer64"]], "KeyColumns" -> None,
"Backend" -> "WolframKernel"], "Options" -> {},
"BackendData" -> Association["ColumnData" -> DataStructure["ColumnTable",
{{TabularColumn[Association["Data" -> {{5.337700872089618, 10.98576811973911,
4.805041489332591, 1.8462731232197358, 5.314719883206279, 6.966733179599361,
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"Integer64"]]}}]]]] -> "Count", ExponentialModel[]]DSolveValue[{n'[t] == -n[t]λ, n[0] == n0}, n[t], t]//FullSimplifyLight intensity attenuated through an absorbing medium:
ModelFit[Tabular[Association["RawSchema" -> Association["ColumnProperties" ->
Association["Distance" -> Association["ElementType" -> "Real64"],
"Intensity" -> Association["ElementType" -> "Real64"]], "KeyColumns" -> None,
"Backend" -> "WolframKernel"], "Options" -> {},
"BackendData" -> Association["ColumnData" -> DataStructure["ColumnTable",
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0.7531656153817168, 0.040717721908193144}, {}, None}, "ElementType" -> "Real64"]]}}]]]] -> "Intensity", ExponentialModel[]]The is the Beer–Lambert law, which solves
:
DSolveValue[{i'[x] == -k i[x], i[0] == i0}, i[x], x]//FullSimplifyPopulation growth when birth rate is proportional to population size:
ModelFit[Tabular[Association["RawSchema" -> Association["ColumnProperties" ->
Association["Time" -> Association["ElementType" -> "Real64"],
"Population" -> Association["ElementType" -> TypeSpecifier["Quantity"]["Real64", "People"]]],
"KeyColumns" -> None, "Backend" -> "WolframKernel"], "Options" -> {},
"BackendData" -> Association["ColumnData" -> DataStructure["ColumnTable",
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None}, "ElementType" -> "Real64"]], TabularColumn[
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368.83910531219584}, {}, None}}, None}, "ElementType" -> TypeSpecifier["Quantity"][
"Real64", "People"]]]}}]]]] -> "Population", ExponentialModel[]]Bacterial population during an early exponential growth phase:
ModelFit[Tabular[Association["RawSchema" -> Association["ColumnProperties" ->
Association["Time" -> Association["ElementType" -> "Real64"],
"Bacteria" -> Association["ElementType" -> "Integer64"]], "KeyColumns" -> None,
"Backend" -> "WolframKernel"], "Options" -> {},
"BackendData" -> Association["ColumnData" -> DataStructure["ColumnTable",
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TabularColumn[Association["Data" -> {{212, 196, 229, 215, 235, 226, 228, 227, 225, 258,
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ModelFit[Tabular[Association["RawSchema" -> Association["ColumnProperties" ->
Association["Time" -> Association["ElementType" -> "Real64"],
"Response" -> Association["ElementType" -> "Real64"]], "KeyColumns" -> None,
"Backend" -> "WolframKernel"], "Options" -> {},
"BackendData" -> Association["ColumnData" -> DataStructure["ColumnTable",
{{TabularColumn[Association["Data" -> {{0., 0.08080808080808081, 0.16161616161616163,
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1.9779949194867361}, {}, None}, "ElementType" -> "Real64"]]}}]]]] -> "Response", ExponentialModel[]]Compare with the response of a system to a UnitStep input:
InputOutputResponse[TransferFunctionModel[{{{1}}, s + λ}, s], UnitStep[t], t]//First//SimplifyPopulation Growth (1)
Retrieve statistics on worldwide population growth before the millennium:
population = CountryData["World", {{"Population"}, {1950, 2000}}]Fit an exponential model to the growth rate:
model = ModelFit[population, ExponentialModel[]]predictions = TransformColumns[population, {"Prediction" -> (model[#Timestamp]&)}]ListPlot[{population -> {"Timestamp", "Value"}, predictions -> {"Timestamp", "Prediction"}}, ...]Predict the population in the year 2020:
model[DateObject[{2020}]]This is greater than the current population:
CountryData["World", {"Population", 2020}]Get the statistics of the population since the year 2001:
millenniumPopulation = CountryData["World", {{"Population"}, {2001, 2020}}]Compare projected continuing growth rate vs the reality:
ListPlot[{millenniumPopulation -> {"Timestamp", "Value"}, TransformColumns[millenniumPopulation, {"Prediction" -> (model[#"Timestamp"]&)}] -> {"Timestamp", "Prediction"}}, ...]To investigate where the trend stopped being exponential, fit over a restricted amount of data from 1980 to 2020 and measure the loss:
allPopulation = Join[population, millenniumPopulation];The loss starts increasing dramatically about 31 years ago, indicating that human population growth stopped being exponential in about 1995.
Table[{endDate, ModelFit[allPopulation[[ ;; endDate]], ExponentialModel[], "Report"]["Loss"]}, {endDate, Range[-40, -1]}]//ListLogPlotSee Also
Related Guides
History
Text
Wolfram Research (2026), ExponentialModel, Wolfram Language function, https://reference.wolfram.com/language/ref/ExponentialModel.html.
CMS
Wolfram Language. 2026. "ExponentialModel." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/ExponentialModel.html.
APA
Wolfram Language. (2026). ExponentialModel. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/ExponentialModel.html
BibTeX
@misc{reference.wolfram_2026_exponentialmodel, author="Wolfram Research", title="{ExponentialModel}", year="2026", howpublished="\url{https://reference.wolfram.com/language/ref/ExponentialModel.html}", note=[Accessed: 12-June-2026]}
BibLaTeX
@online{reference.wolfram_2026_exponentialmodel, organization={Wolfram Research}, title={ExponentialModel}, year={2026}, url={https://reference.wolfram.com/language/ref/ExponentialModel.html}, note=[Accessed: 12-June-2026]}